
## Importing Libraries
import pandas as pd ## To work with data
import numpy as np ## For faster matrix manipulations/operations
from matplotlib import pyplot as plt ## For Visualization


## Reading the first Dataset and taking a sneak peak into the data
df1 = pd.read_csv("data1.csv")
df1.head()


df1 = pd.read_csv("data1.csv", sep = ';')
df1.head()


pd.set_option('display.max_columns', None)
df1.head()


## Reading the second Dataset and taking a sneak peak into the data
df2 = pd.read_csv("data2.csv", sep = ';')
df2.head()


## Reading the third Dataset and taking a sneak peak into the data
df3 = pd.read_csv("data3.csv", sep = ';')
df3.head()


## df1 and df2 have different attributes of same students. We will join these two datasets on their IDs
df = pd.merge(df1, df2, on='ID')


## df3 consists data for students that are not present in df. We will concatinate these records in df and
## get one dataframe that will all the data available to us
## It is also a goo idea to take a look into the dataset's shape, i.e., no of records and no of attributes
df = pd.concat([df, df3])
df.shape

(4424, 38)


## Taking a look into the final dataset
df.head()


## Looking at column datatypes can serve as a good starting point for data cleaning process
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4424 entries, 0 to 999
Data columns (total 38 columns):
 #   Column                                          Non-Null Count  Dtype  
---  ------                                          --------------  -----  
 0   ID                                              4424 non-null   int64  
 1   Marital status                                  4424 non-null   int64  
 2   Application mode                                4424 non-null   int64  
 3   Application order                               4424 non-null   int64  
 4   Course                                          4424 non-null   int64  
 5   Daytime/evening attendance                      4424 non-null   object 
 6   Previous qualification                          4424 non-null   int64  
 7   Previous qualification (grade)                  4424 non-null   float64
 8   Nationality                                     4424 non-null   int64  
 9   Mother's qualification                          4424 non-null   int64  
 10  Father's qualification                          4424 non-null   object 
 11  Mother's occupation                             4424 non-null   int64  
 12  Father's occupation                             4424 non-null   int64  
 13  Admission grade                                 4424 non-null   float64
 14  Displaced                                       4424 non-null   int64  
 15  Educational special needs                       4424 non-null   int64  
 16  Debtor                                          4424 non-null   int64  
 17  Tuition fees up to date                         3560 non-null   float64
 18  Gender                                          4424 non-null   int64  
 19  Scholarship holder                              4424 non-null   int64  
 20  Age at enrollment                               4424 non-null   int64  
 21  International                                   3447 non-null   float64
 22  Curricular units 1st sem (credited)             4424 non-null   int64  
 23  Curricular units 1st sem (enrolled)             4424 non-null   int64  
 24  Curricular units 1st sem (evaluations)          4424 non-null   int64  
 25  Curricular units 1st sem (approved)             4424 non-null   int64  
 26  Curricular units 1st sem (grade)                4424 non-null   float64
 27  Curricular units 1st sem (without evaluations)  4424 non-null   int64  
 28  Curricular units 2nd sem (credited)             4424 non-null   int64  
 29  Curricular units 2nd sem (enrolled)             4424 non-null   int64  
 30  Curricular units 2nd sem (evaluations)          4424 non-null   int64  
 31  Curricular units 2nd sem (approved)             4424 non-null   int64  
 32  Curricular units 2nd sem (grade)                4424 non-null   float64
 33  Curricular units 2nd sem (without evaluations)  4424 non-null   int64  
 34  Unemployment rate                               4424 non-null   float64
 35  Inflation rate                                  4424 non-null   float64
 36  GDP                                             4424 non-null   float64
 37  Target                                          4424 non-null   object 
dtypes: float64(9), int64(26), object(3)
memory usage: 1.3+ MB


for col in df.columns:
    if df[col].dtype == 'object':
        print(col, ':')
        print(df[col].unique())

Daytime/evening attendance :
['1' '0' 'Y' 1 0]
Father's qualification :
['39' '34' '37' '1' '38' '19' '2' '3' '35' '11' '25' '5' '26' '40' '41'
 '4' '22' '20' '29' '31' '36' '6' '12' '9' '27' '14' '10' '44' '33' '13'
 '30' '          ' '18' '43' 1 19 37 38 4 3 34 2 39 12 5 11 9 36 14 43 22
 42 41 10 6 40 30]
Target :
['Drpout' 'Dropout' 'Graduate' 'Enrolled' 'Dropot' 'Grduate' 'enroled']


## Solving data inconsistancy in 'Daytime/evening attendance' column
df.loc[:, 'Daytime/evening attendance'] = df['Daytime/evening attendance'].map({'1':1, 1:1, 0:0, '0':0, 'Y':1})


## Function to handle data inconsistancy in "father's qualification" column
def clean_father_qualification(x):
    ## For numerical data or text representation of the same
    try:
        return int(x)
    ## For whitespaces
    except:
        return None

df.loc[:, "Father's qualification"] = df.loc[:, "Father's qualification"].apply(clean_father_qualification)


## Handling data inconsistancy in "Target" column
target_column_cleaning_dict = {
    'Drpout' : 'Dropout'
    , 'Dropout' : 'Dropout'
    , 'Graduate' : 'Graduate'
    , 'Enrolled' : 'Enrolled'
    , 'Dropot' : 'Dropout'
    , 'Grduate' : 'Graduate'
    , 'enroled' : 'Enrolled'
}
df.loc[:, "Target"] = df.loc[:, "Target"].map(target_column_cleaning_dict)


## Check for column which have skewed distribution
df.skew(numeric_only = True)

ID                                                 0.000000
Marital status                                     4.399764
Application mode                                   0.393036
Application order                                  1.881050
Course                                            -3.809135
Daytime/evening attendance                        -2.507238
Previous qualification                             2.871207
Previous qualification (grade)                     0.312867
Nationality                                       10.703998
Mother's qualification                             0.001978
Father's qualification                            -0.298811
Mother's occupation                                5.339227
Father's occupation                                5.395173
Admission grade                                    0.530600
Displaced                                         -0.194468
Educational special needs                          9.154976
Debtor                                             2.434652
Tuition fees up to date                           -1.979866
Gender                                             0.621279
Scholarship holder                                 1.164871
Age at enrollment                                 12.428523
International                                      5.328606
Curricular units 1st sem (credited)                4.169049
Curricular units 1st sem (enrolled)                1.619041
Curricular units 1st sem (evaluations)             0.976637
Curricular units 1st sem (approved)                0.766262
Curricular units 1st sem (grade)                  -1.568146
Curricular units 1st sem (without evaluations)     8.207403
Curricular units 2nd sem (credited)                4.634820
Curricular units 2nd sem (enrolled)                0.788114
Curricular units 2nd sem (evaluations)             0.336497
Curricular units 2nd sem (approved)                0.306279
Curricular units 2nd sem (grade)                  -1.313650
Curricular units 2nd sem (without evaluations)     7.267701
Unemployment rate                                  0.212051
Inflation rate                                     0.252375
GDP                                               -0.394068
dtype: float64


## List of columns with high skew
skew_cols = [
    'Nationality'
    , 'Educational special needs'
    , 'Age at enrollment'
    , 'Curricular units 1st sem (without evaluations)'
    , 'Curricular units 2nd sem (without evaluations)'
]

## Checking value counts for high skew columns
for col in skew_cols:
    print(col, df[col].value_counts().sort_index(), sep = '\n')
    print('*'*80)

Nationality
1      4314
2         2
6        13
11        3
13        1
14        1
17        1
21        2
22       13
24        5
25        2
26       14
32        1
41       38
62        2
100       3
101       2
103       3
105       2
108       1
109       1
Name: Nationality, dtype: int64
********************************************************************************
Educational special needs
0    4373
1      51
Name: Educational special needs, dtype: int64
********************************************************************************
Age at enrollment
17        5
18     1036
19      911
20      599
21      322
22      171
23      108
24      131
25       93
26       94
27       91
28       83
29       66
30       49
31       55
32       61
33       28
34       60
35       49
36       35
37       42
38       29
39       38
40       23
41       31
42       13
43       25
44       19
45       22
46       12
47       18
48       11
49       13
50       16
51        7
52        4
53        7
54        7
55        5
57        2
58        3
59        3
60        2
61        1
62        1
70        1
222       3
333      17
444       2
Name: Age at enrollment, dtype: int64
********************************************************************************
Curricular units 1st sem (without evaluations)
0     4130
1      153
2       79
3       23
4       15
5        5
6        6
7        6
8        4
10       1
12       2
Name: Curricular units 1st sem (without evaluations), dtype: int64
********************************************************************************
Curricular units 2nd sem (without evaluations)
0     4142
1      140
2       48
3       35
4       21
5       17
6        8
7        5
8        6
12       2
Name: Curricular units 2nd sem (without evaluations), dtype: int64
********************************************************************************


## Visuaization library for interactive graphs
## Feel free to zoom in and varify first observation
import plotly.express as px
fig = px.box(
    df
    , x = 'Age at enrollment'
    , title = "Data Distribution of 'Age at enrollment' column"
)
fig.update_yaxes(title = "Number of records", tickangle=0)
fig.show()


print("Data Inconsistency records %:", (df[df['Age at enrollment']>100].shape[0]/df.shape[0])*100)

Data Inconsistency records %: 0.4972875226039783


## Only keeping records where age is < 100yrs
df = df[df['Age at enrollment']<100]


## Check number of null values in each column
df.isna().sum()

ID                                                  0
Marital status                                      0
Application mode                                    0
Application order                                   0
Course                                              0
Daytime/evening attendance                          0
Previous qualification                              0
Previous qualification (grade)                      0
Nationality                                         0
Mother's qualification                              0
Father's qualification                              1
Mother's occupation                                 0
Father's occupation                                 0
Admission grade                                     0
Displaced                                           0
Educational special needs                           0
Debtor                                              0
Tuition fees up to date                           864
Gender                                              0
Scholarship holder                                  0
Age at enrollment                                   0
International                                     977
Curricular units 1st sem (credited)                 0
Curricular units 1st sem (enrolled)                 0
Curricular units 1st sem (evaluations)              0
Curricular units 1st sem (approved)                 0
Curricular units 1st sem (grade)                    0
Curricular units 1st sem (without evaluations)      0
Curricular units 2nd sem (credited)                 0
Curricular units 2nd sem (enrolled)                 0
Curricular units 2nd sem (evaluations)              0
Curricular units 2nd sem (approved)                 0
Curricular units 2nd sem (grade)                    0
Curricular units 2nd sem (without evaluations)      0
Unemployment rate                                   0
Inflation rate                                      0
GDP                                                 0
Target                                              0
dtype: int64


## Checking the relationship between 'Nationality'and 'International' column
print("Nationality\tInternational")
for group, data in df.groupby('Nationality'):
    print(group, data['International'].unique(), sep = '\t\t')

Nationality	International
1		[ 0. nan]
2		[1.]
6		[1.]
11		[1.]
13		[1.]
14		[1.]
17		[1.]
21		[1.]
22		[1.]
24		[1.]
25		[1.]
26		[1.]
32		[1.]
41		[1.]
62		[1.]
100		[1.]
101		[1.]
103		[1.]
105		[1.]
108		[1.]
109		[1.]


## Imputing International column based on Nationality
df.loc[:, 'International'] = df.apply(lambda row: 0 if row['Nationality'] == 1 else 1, axis = 1)


for col in df.columns[1:]:
    print(col, "Tuition fees up to date", sep = '\t')
    for group, data in df.groupby(col):
        print(group, data['Tuition fees up to date'].unique(), sep = '\t\t')
    print('*'*100)

Marital status	Tuition fees up to date
1		[ 1.  0. nan]
2		[ 1.  0. nan]
3		[ 1. nan]
4		[ 0.  1. nan]
5		[ 1.  0. nan]
6		[1. 0.]
****************************************************************************************************
Application mode	Tuition fees up to date
1		[ 1.  0. nan]
2		[0. 1.]
5		[ 1. nan]
7		[ 0.  1. nan]
10		[ 1.  0. nan]
15		[ 1.  0. nan]
16		[ 1. nan]
17		[ 1.  0. nan]
18		[ 0.  1. nan]
26		[1.]
27		[1.]
39		[ 0.  1. nan]
42		[ 1.  0. nan]
43		[ 1.  0. nan]
44		[ 1.  0. nan]
51		[ 1.  0. nan]
53		[ 1.  0. nan]
57		[1.]
****************************************************************************************************
Application order	Tuition fees up to date
0		[1.]
1		[ 1.  0. nan]
2		[ 0.  1. nan]
3		[ 0.  1. nan]
4		[ 0.  1. nan]
5		[ 0.  1. nan]
6		[ 1.  0. nan]
9		[1.]
****************************************************************************************************
Course	Tuition fees up to date
33		[ 0.  1. nan]
171		[ 1.  0. nan]
8014		[ 0.  1. nan]
9003		[ 1.  0. nan]
9070		[ 1.  0. nan]
9085		[ 1.  0. nan]
9119		[ 1.  0. nan]
9130		[ 1.  0. nan]
9147		[ 0.  1. nan]
9238		[ 1.  0. nan]
9254		[ 1.  0. nan]
9500		[ 1.  0. nan]
9556		[ 0.  1. nan]
9670		[ 0.  1. nan]
9773		[ 1.  0. nan]
9853		[ 1.  0. nan]
9991		[ 1.  0. nan]
****************************************************************************************************
Daytime/evening attendance	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 0.  1. nan]
****************************************************************************************************
Previous qualification	Tuition fees up to date
1		[ 1.  0. nan]
2		[ 0.  1. nan]
3		[ 0.  1. nan]
4		[ 1. nan]
5		[0.]
6		[ 1. nan]
9		[ 1.  0. nan]
10		[1. 0.]
12		[ 1.  0. nan]
14		[0.]
15		[ 0. nan]
19		[ 0.  1. nan]
38		[ 1.  0. nan]
39		[ 1.  0. nan]
40		[ 0.  1. nan]
42		[ 1.  0. nan]
43		[ 1. nan]
****************************************************************************************************
Previous qualification (grade)	Tuition fees up to date
95.0		[0.]
96.0		[nan]
97.0		[nan]
99.0		[ 0. nan]
100.0		[ 1.  0. nan]
101.0		[ 0.  1. nan]
102.0		[ 1. nan]
103.0		[1.]
105.0		[ 1. nan]
106.0		[ 1.  0. nan]
107.0		[ 1. nan]
108.0		[ 1.  0. nan]
109.0		[1. 0.]
110.0		[ 1.  0. nan]
111.0		[ 0.  1. nan]
112.0		[ 1.  0. nan]
113.0		[ 0.  1. nan]
114.0		[1. 0.]
115.0		[ 1.  0. nan]
116.0		[ 1.  0. nan]
117.0		[ 1.  0. nan]
117.4		[1.]
118.0		[ 1.  0. nan]
118.9		[1.]
119.0		[ 1.  0. nan]
119.1		[1.]
120.0		[ 0.  1. nan]
121.0		[ 1.  0. nan]
122.0		[ 1.  0. nan]
123.0		[ 0.  1. nan]
123.9		[1.]
124.0		[ 1.  0. nan]
124.4		[1.]
125.0		[ 1.  0. nan]
126.0		[ 1.  0. nan]
126.6		[1.]
127.0		[ 1.  0. nan]
128.0		[ 1.  0. nan]
129.0		[ 1.  0. nan]
130.0		[ 0.  1. nan]
131.0		[ 1.  0. nan]
132.0		[ 1.  0. nan]
133.0		[ 1.  0. nan]
133.1		[ 0.  1. nan]
133.8		[1.]
134.0		[ 1.  0. nan]
135.0		[ 0.  1. nan]
136.0		[ 1.  0. nan]
137.0		[ 1.  0. nan]
138.0		[ 0.  1. nan]
138.6		[nan]
138.7		[1.]
139.0		[ 1.  0. nan]
139.3		[1.]
140.0		[ 1.  0. nan]
140.8		[nan]
141.0		[ 1.  0. nan]
142.0		[ 1.  0. nan]
143.0		[ 0.  1. nan]
144.0		[ 1.  0. nan]
145.0		[ 0.  1. nan]
145.7		[1.]
146.0		[ 1. nan]
147.0		[ 0.  1. nan]
148.0		[ 1.  0. nan]
148.9		[1.]
149.0		[ 1.  0. nan]
150.0		[ 1.  0. nan]
151.0		[ 1. nan]
152.0		[ 1.  0. nan]
153.0		[ 1. nan]
154.0		[ 1. nan]
154.4		[0.]
155.0		[ 1. nan]
156.0		[ 1.  0. nan]
157.0		[ 1. nan]
158.0		[ 1.  0. nan]
159.0		[ 1.  0. nan]
160.0		[ 1.  0. nan]
161.0		[ 1.  0. nan]
162.0		[ 1. nan]
163.0		[1.]
163.3		[1.]
164.0		[ 1. nan]
165.0		[1.]
166.0		[nan]
167.0		[1.]
168.0		[ 0.  1. nan]
169.0		[1.]
170.0		[ 1.  0. nan]
172.0		[ 1. nan]
174.0		[1.]
175.0		[1.]
176.0		[1.]
177.0		[1.]
178.0		[ 1. nan]
180.0		[ 1.  0. nan]
182.0		[1.]
184.4		[nan]
188.0		[nan]
190.0		[1.]
****************************************************************************************************
Nationality	Tuition fees up to date
1		[ 0.  1. nan]
2		[1.]
6		[ 0.  1. nan]
11		[1.]
13		[1.]
14		[1.]
17		[0.]
21		[1.]
22		[ 0.  1. nan]
24		[ 1.  0. nan]
25		[1.]
26		[ 1.  0. nan]
32		[1.]
41		[ 1.  0. nan]
62		[1.]
100		[ 1. nan]
101		[ 1. nan]
103		[1.]
105		[1. 0.]
108		[1.]
109		[nan]
****************************************************************************************************
Mother's qualification	Tuition fees up to date
1		[ 1.  0. nan]
2		[ 1.  0. nan]
3		[ 0.  1. nan]
4		[ 0.  1. nan]
5		[ 1.  0. nan]
6		[1. 0.]
9		[ 1.  0. nan]
10		[ 1. nan]
11		[ 1. nan]
12		[ 0.  1. nan]
14		[1. 0.]
18		[0.]
19		[ 1.  0. nan]
22		[1.]
26		[1.]
27		[1.]
29		[ 1. nan]
30		[1. 0.]
34		[ 1.  0. nan]
35		[0. 1.]
36		[ 0.  1. nan]
37		[ 0.  1. nan]
38		[ 1.  0. nan]
39		[ 1.  0. nan]
40		[ 0.  1. nan]
41		[ 0.  1. nan]
42		[ 1. nan]
43		[ 1. nan]
44		[1.]
****************************************************************************************************
Father's qualification	Tuition fees up to date
1.0		[ 0.  1. nan]
2.0		[ 1.  0. nan]
3.0		[ 1.  0. nan]
4.0		[ 1.  0. nan]
5.0		[ 1.  0. nan]
6.0		[ 1. nan]
9.0		[ 1. nan]
10.0		[ 1. nan]
11.0		[ 0.  1. nan]
12.0		[ 1.  0. nan]
13.0		[1.]
14.0		[ 1. nan]
18.0		[0.]
19.0		[ 1.  0. nan]
20.0		[1.]
22.0		[ 1.  0. nan]
25.0		[1.]
26.0		[0. 1.]
27.0		[1.]
29.0		[0. 1.]
30.0		[ 1. nan]
31.0		[1.]
33.0		[0.]
34.0		[ 1.  0. nan]
35.0		[0. 1.]
36.0		[ 0.  1. nan]
37.0		[ 0.  1. nan]
38.0		[ 1.  0. nan]
39.0		[ 1.  0. nan]
40.0		[ 1. nan]
41.0		[ 0. nan]
42.0		[nan]
43.0		[ 1. nan]
44.0		[0.]
****************************************************************************************************
Mother's occupation	Tuition fees up to date
0		[ 1.  0. nan]
1		[ 1.  0. nan]
2		[ 1.  0. nan]
3		[ 0.  1. nan]
4		[ 0.  1. nan]
5		[ 1.  0. nan]
6		[ 1.  0. nan]
7		[ 1.  0. nan]
8		[ 1.  0. nan]
9		[ 0.  1. nan]
10		[ 1. nan]
90		[ 0.  1. nan]
99		[ 1.  0. nan]
122		[1.]
123		[ 1. nan]
125		[1.]
131		[1.]
132		[ 1. nan]
134		[1.]
141		[ 1. nan]
143		[1.]
144		[1.]
151		[1.]
152		[1.]
153		[1.]
171		[0.]
173		[nan]
175		[ 1.  0. nan]
191		[ 1.  0. nan]
192		[ 1. nan]
193		[1.]
194		[1.]
****************************************************************************************************
Father's occupation	Tuition fees up to date
0		[ 1.  0. nan]
1		[ 1.  0. nan]
2		[ 1.  0. nan]
3		[ 1.  0. nan]
4		[ 0.  1. nan]
5		[ 1.  0. nan]
6		[ 1.  0. nan]
7		[ 0.  1. nan]
8		[ 1.  0. nan]
9		[ 1.  0. nan]
10		[ 1.  0. nan]
90		[ 0.  1. nan]
99		[ 1.  0. nan]
101		[1.]
102		[1.]
103		[1.]
112		[1.]
114		[1.]
121		[nan]
122		[ 1. nan]
123		[1.]
124		[1.]
131		[1.]
132		[1.]
134		[1.]
135		[1.]
141		[1.]
143		[1.]
144		[ 1. nan]
151		[1.]
152		[1.]
153		[1.]
154		[1.]
161		[1.]
163		[1. 0.]
171		[ 0.  1. nan]
172		[ 1. nan]
174		[1.]
175		[ 1. nan]
181		[1.]
182		[1.]
183		[ 1. nan]
192		[ 1. nan]
193		[0. 1.]
194		[1.]
195		[nan]
****************************************************************************************************
Admission grade	Tuition fees up to date
95.0		[ 0.  1. nan]
95.1		[0.]
95.5		[0. 1.]
95.8		[0.]
96.0		[ 0.  1. nan]
96.1		[1.]
96.7		[1.]
97.0		[ 0.  1. nan]
97.2		[1.]
97.4		[0.]
97.5		[nan]
98.0		[ 1. nan]
98.1		[nan]
98.5		[1.]
98.6		[1. 0.]
98.7		[nan]
98.9		[ 1. nan]
99.0		[ 1. nan]
99.3		[1.]
99.5		[0.]
99.6		[1.]
99.7		[0.]
100.0		[ 1.  0. nan]
100.1		[ 0. nan]
100.2		[0.]
100.5		[ 1. nan]
100.6		[ 1. nan]
100.7		[nan]
100.8		[1.]
100.9		[1.]
101.0		[ 1.  0. nan]
101.3		[ 1. nan]
101.5		[0. 1.]
101.6		[1.]
101.7		[1.]
101.8		[0. 1.]
102.0		[1.]
102.2		[1.]
102.4		[1.]
102.5		[ 0.  1. nan]
102.6		[nan]
102.8		[1.]
103.0		[ 1.  0. nan]
103.4		[ 1. nan]
103.5		[ 0.  1. nan]
103.6		[1.]
103.7		[1.]
103.8		[1.]
104.0		[ 1.  0. nan]
104.1		[0. 1.]
104.2		[1.]
104.5		[ 1. nan]
104.6		[0.]
104.7		[nan]
104.8		[1. 0.]
105.0		[ 1.  0. nan]
105.1		[1.]
105.2		[ 1. nan]
105.3		[0. 1.]
105.4		[1.]
105.5		[1. 0.]
105.6		[nan]
105.7		[1.]
105.8		[1.]
105.9		[ 1. nan]
106.0		[ 1.  0. nan]
106.1		[1. 0.]
106.2		[nan]
106.3		[0.]
106.4		[ 1. nan]
106.5		[1. 0.]
106.6		[ 1. nan]
106.7		[1.]
106.8		[ 1. nan]
107.0		[ 0.  1. nan]
107.1		[ 1. nan]
107.2		[nan]
107.3		[1.]
107.4		[0. 1.]
107.5		[ 1.  0. nan]
107.6		[nan]
107.7		[0. 1.]
107.8		[1. 0.]
108.0		[1.]
108.2		[1.]
108.3		[ 0.  1. nan]
108.4		[1.]
108.5		[1.]
108.6		[0.]
108.7		[ 1.  0. nan]
108.8		[1.]
109.0		[ 1.  0. nan]
109.1		[ 0.  1. nan]
109.3		[ 1.  0. nan]
109.4		[1.]
109.5		[1.]
109.6		[1.]
109.7		[1.]
109.8		[0. 1.]
109.9		[0.]
110.0		[ 0.  1. nan]
110.1		[ 1. nan]
110.2		[ 1. nan]
110.3		[ 1.  0. nan]
110.4		[1. 0.]
110.5		[ 1. nan]
110.6		[1.]
110.7		[ 1. nan]
110.8		[ 1. nan]
111.0		[ 0.  1. nan]
111.1		[ 1.  0. nan]
111.3		[ 1.  0. nan]
111.4		[1. 0.]
111.5		[ 0.  1. nan]
111.6		[ 1. nan]
111.7		[1. 0.]
111.8		[ 1. nan]
111.9		[ 1.  0. nan]
112.0		[ 1.  0. nan]
112.1		[1. 0.]
112.2		[ 1. nan]
112.3		[1. 0.]
112.4		[1.]
112.5		[ 1. nan]
112.6		[ 1. nan]
112.7		[1.]
112.8		[1.]
112.9		[ 1.  0. nan]
113.0		[1. 0.]
113.1		[0. 1.]
113.2		[ 1.  0. nan]
113.3		[ 1. nan]
113.4		[ 1. nan]
113.5		[ 1. nan]
113.6		[ 1. nan]
113.7		[ 1.  0. nan]
113.8		[0. 1.]
113.9		[ 1.  0. nan]
114.0		[ 1.  0. nan]
114.2		[ 1. nan]
114.3		[ 1.  0. nan]
114.4		[ 1. nan]
114.5		[ 1. nan]
114.6		[ 1.  0. nan]
114.7		[ 1. nan]
114.8		[ 1.  0. nan]
114.9		[ 1.  0. nan]
115.0		[ 1.  0. nan]
115.1		[ 0.  1. nan]
115.2		[ 1.  0. nan]
115.3		[1.]
115.4		[ 1. nan]
115.5		[ 1.  0. nan]
115.6		[0. 1.]
115.7		[0. 1.]
115.8		[ 0.  1. nan]
115.9		[ 1. nan]
116.0		[ 1.  0. nan]
116.1		[ 0.  1. nan]
116.2		[1. 0.]
116.3		[ 0.  1. nan]
116.4		[0. 1.]
116.5		[ 0.  1. nan]
116.6		[ 1.  0. nan]
116.7		[1.]
116.8		[ 1.  0. nan]
116.9		[ 1.  0. nan]
117.0		[ 1.  0. nan]
117.1		[ 1. nan]
117.2		[ 1. nan]
117.3		[1.]
117.4		[ 1. nan]
117.5		[ 1.  0. nan]
117.6		[1.]
117.7		[ 1. nan]
117.8		[ 0.  1. nan]
117.9		[ 0.  1. nan]
118.0		[ 1.  0. nan]
118.1		[ 1.  0. nan]
118.2		[ 1. nan]
118.3		[ 1.  0. nan]
118.4		[ 1.  0. nan]
118.5		[ 1. nan]
118.6		[ 1. nan]
118.7		[ 1. nan]
118.8		[ 1.  0. nan]
118.9		[ 1. nan]
119.0		[ 1.  0. nan]
119.1		[ 1.  0. nan]
119.2		[1.]
119.3		[ 1. nan]
119.4		[ 0.  1. nan]
119.5		[1. 0.]
119.6		[ 1.  0. nan]
119.7		[ 1. nan]
119.8		[ 1. nan]
119.9		[1. 0.]
120.0		[ 1.  0. nan]
120.1		[ 0.  1. nan]
120.2		[ 1. nan]
120.3		[ 1.  0. nan]
120.4		[ 0.  1. nan]
120.5		[ 1.  0. nan]
120.6		[ 1.  0. nan]
120.7		[ 1.  0. nan]
120.8		[ 1. nan]
120.9		[1.]
121.0		[ 1.  0. nan]
121.1		[1.]
121.2		[ 1. nan]
121.3		[ 1. nan]
121.4		[ 1.  0. nan]
121.5		[ 1.  0. nan]
121.6		[ 1. nan]
121.7		[ 1.  0. nan]
121.8		[0. 1.]
121.9		[ 0.  1. nan]
122.0		[ 1.  0. nan]
122.1		[ 1.  0. nan]
122.2		[1. 0.]
122.3		[ 1.  0. nan]
122.4		[ 1. nan]
122.5		[ 1.  0. nan]
122.6		[ 1.  0. nan]
122.7		[ 1.  0. nan]
122.8		[ 0.  1. nan]
122.9		[1. 0.]
123.0		[ 1.  0. nan]
123.1		[1. 0.]
123.2		[ 1. nan]
123.3		[ 1.  0. nan]
123.4		[ 1. nan]
123.5		[1. 0.]
123.6		[ 1. nan]
123.7		[ 1.  0. nan]
123.8		[ 0.  1. nan]
123.9		[ 1. nan]
124.0		[ 1.  0. nan]
124.1		[ 1. nan]
124.2		[ 1.  0. nan]
124.3		[ 1.  0. nan]
124.4		[ 1. nan]
124.5		[ 0.  1. nan]
124.6		[ 1.  0. nan]
124.7		[ 1. nan]
124.8		[ 1.  0. nan]
124.9		[ 1. nan]
125.0		[ 1.  0. nan]
125.1		[ 0.  1. nan]
125.2		[ 1. nan]
125.3		[ 1. nan]
125.4		[ 0.  1. nan]
125.5		[ 1.  0. nan]
125.6		[ 1. nan]
125.7		[ 0.  1. nan]
125.8		[0. 1.]
125.9		[ 1. nan]
126.0		[ 1.  0. nan]
126.1		[ 1.  0. nan]
126.2		[ 1.  0. nan]
126.3		[ 1.  0. nan]
126.4		[ 1. nan]
126.5		[ 1.  0. nan]
126.6		[ 1.  0. nan]
126.7		[ 1.  0. nan]
126.8		[ 1.  0. nan]
126.9		[ 1. nan]
127.0		[ 1.  0. nan]
127.1		[ 1.  0. nan]
127.2		[ 1.  0. nan]
127.3		[ 1.  0. nan]
127.4		[ 0.  1. nan]
127.5		[ 0.  1. nan]
127.6		[ 1. nan]
127.7		[ 1.  0. nan]
127.8		[ 1.  0. nan]
127.9		[ 1.  0. nan]
128.0		[ 1.  0. nan]
128.1		[ 1. nan]
128.2		[ 1.  0. nan]
128.3		[ 1.  0. nan]
128.4		[ 1.  0. nan]
128.5		[ 1.  0. nan]
128.6		[1.]
128.7		[1.]
128.8		[ 1. nan]
128.9		[ 1. nan]
129.0		[ 1.  0. nan]
129.1		[ 1. nan]
129.2		[ 1.  0. nan]
129.3		[ 1. nan]
129.4		[ 1.  0. nan]
129.5		[ 1.  0. nan]
129.6		[1.]
129.7		[ 1. nan]
129.8		[ 1.  0. nan]
129.9		[ 1. nan]
130.0		[ 0.  1. nan]
130.1		[0. 1.]
130.2		[ 1. nan]
130.3		[ 1. nan]
130.4		[ 1. nan]
130.5		[ 1. nan]
130.6		[1. 0.]
130.7		[ 1. nan]
130.8		[ 1.  0. nan]
130.9		[ 0.  1. nan]
131.0		[ 1.  0. nan]
131.1		[ 1. nan]
131.2		[ 1. nan]
131.3		[ 1. nan]
131.4		[ 1.  0. nan]
131.5		[ 1. nan]
131.6		[ 1. nan]
131.7		[ 1.  0. nan]
131.8		[ 1.  0. nan]
131.9		[ 1. nan]
132.0		[ 0.  1. nan]
132.1		[ 1. nan]
132.2		[1.]
132.3		[ 1. nan]
132.4		[ 1.  0. nan]
132.5		[ 1.  0. nan]
132.6		[ 1. nan]
132.7		[ 1. nan]
132.8		[ 0.  1. nan]
132.9		[ 1.  0. nan]
133.0		[ 1. nan]
133.1		[ 1. nan]
133.2		[ 0.  1. nan]
133.3		[ 0.  1. nan]
133.4		[ 1.  0. nan]
133.5		[ 1.  0. nan]
133.6		[0. 1.]
133.7		[ 1. nan]
133.8		[ 1. nan]
133.9		[ 1.  0. nan]
134.0		[ 1. nan]
134.1		[ 0.  1. nan]
134.2		[ 1. nan]
134.3		[1.]
134.4		[ 1.  0. nan]
134.5		[ 1. nan]
134.6		[ 1. nan]
134.7		[1. 0.]
134.8		[ 1. nan]
134.9		[1.]
135.0		[ 1. nan]
135.1		[ 1. nan]
135.2		[1.]
135.3		[1.]
135.4		[1.]
135.5		[1. 0.]
135.6		[ 1. nan]
135.7		[1.]
135.8		[ 1. nan]
135.9		[ 1. nan]
136.0		[ 1.  0. nan]
136.1		[ 1.  0. nan]
136.2		[ 1. nan]
136.3		[ 1.  0. nan]
136.4		[1.]
136.5		[ 1. nan]
136.6		[ 1. nan]
136.7		[ 1. nan]
136.8		[ 1. nan]
136.9		[ 1. nan]
137.0		[ 1. nan]
137.1		[ 1. nan]
137.2		[0. 1.]
137.3		[ 1. nan]
137.4		[1.]
137.5		[ 1.  0. nan]
137.6		[1.]
137.7		[0.]
137.8		[ 1. nan]
137.9		[0.]
138.0		[ 1.  0. nan]
138.1		[ 1.  0. nan]
138.2		[0.]
138.3		[ 1.  0. nan]
138.4		[ 1.  0. nan]
138.5		[1.]
138.6		[ 1. nan]
138.7		[ 1. nan]
138.8		[ 1.  0. nan]
138.9		[ 1. nan]
139.0		[ 1. nan]
139.1		[1.]
139.2		[1.]
139.3		[nan]
139.4		[1. 0.]
139.5		[1.]
139.6		[0.]
139.7		[ 1. nan]
139.8		[ 1. nan]
139.9		[1.]
140.0		[ 1.  0. nan]
140.1		[1.]
140.2		[ 1. nan]
140.3		[1.]
140.4		[ 1. nan]
140.5		[ 0.  1. nan]
140.7		[1.]
140.8		[ 1. nan]
140.9		[ 1. nan]
141.0		[ 1.  0. nan]
141.1		[1.]
141.2		[ 1. nan]
141.3		[ 0.  1. nan]
141.4		[ 1.  0. nan]
141.5		[ 1.  0. nan]
141.6		[1.]
141.7		[ 1.  0. nan]
141.8		[1.]
142.0		[1.]
142.2		[ 1. nan]
142.3		[ 1. nan]
142.4		[1.]
142.5		[ 1.  0. nan]
142.6		[1.]
142.7		[1.]
142.8		[ 1.  0. nan]
143.0		[ 1. nan]
143.1		[1.]
143.2		[1.]
143.3		[ 0.  1. nan]
143.4		[1. 0.]
143.5		[1.]
143.6		[ 1. nan]
143.7		[1.]
143.8		[ 1. nan]
143.9		[1.]
144.0		[1.]
144.1		[0. 1.]
144.2		[1.]
144.3		[1.]
144.4		[1.]
144.5		[1.]
144.6		[0. 1.]
144.7		[ 1. nan]
144.8		[1.]
144.9		[1. 0.]
145.0		[ 1. nan]
145.2		[ 1. nan]
145.3		[ 1. nan]
145.4		[nan]
145.6		[1.]
145.7		[1.]
145.8		[nan]
145.9		[1.]
146.0		[1.]
146.1		[ 1. nan]
146.2		[ 1. nan]
146.3		[nan]
146.4		[ 1. nan]
146.5		[1. 0.]
146.6		[1.]
146.7		[ 1. nan]
146.8		[1.]
146.9		[1.]
147.0		[ 1. nan]
147.2		[1.]
147.3		[1.]
147.5		[1.]
147.6		[ 1. nan]
147.7		[ 1. nan]
147.8		[ 1. nan]
147.9		[1.]
148.0		[ 1.  0. nan]
148.2		[1.]
148.3		[1.]
148.4		[ 1. nan]
148.5		[ 1. nan]
148.6		[1.]
148.7		[1. 0.]
148.8		[ 1. nan]
148.9		[1.]
149.0		[0. 1.]
149.2		[nan]
149.3		[ 1. nan]
149.4		[0.]
149.5		[1. 0.]
149.7		[1.]
149.8		[ 1. nan]
149.9		[ 1.  0. nan]
150.0		[ 0.  1. nan]
150.1		[nan]
150.2		[ 0.  1. nan]
150.3		[ 1. nan]
150.4		[nan]
150.5		[ 1. nan]
150.6		[nan]
150.8		[1.]
150.9		[1.]
151.0		[ 1.  0. nan]
151.1		[1.]
151.2		[1.]
151.3		[1.]
151.4		[1. 0.]
151.5		[0.]
151.6		[1.]
151.7		[1.]
151.9		[nan]
152.0		[ 1.  0. nan]
152.1		[1.]
152.3		[1.]
152.4		[ 1. nan]
152.5		[nan]
152.7		[0.]
152.8		[ 1. nan]
152.9		[1.]
153.0		[ 0.  1. nan]
153.1		[1.]
153.2		[nan]
153.5		[1. 0.]
153.6		[1.]
153.7		[1.]
153.8		[1.]
153.9		[ 0. nan]
154.0		[ 1. nan]
154.1		[1.]
154.3		[1.]
154.4		[ 1.  0. nan]
154.5		[1.]
154.6		[0. 1.]
154.7		[1.]
154.8		[1.]
154.9		[0.]
155.0		[ 1. nan]
155.1		[ 0.  1. nan]
155.2		[nan]
155.3		[ 1. nan]
155.5		[ 0.  1. nan]
155.6		[nan]
155.7		[ 1. nan]
155.8		[1.]
156.0		[ 0. nan]
156.1		[ 0. nan]
156.2		[1.]
156.3		[nan]
156.4		[nan]
156.5		[ 1. nan]
156.8		[1.]
156.9		[1.]
157.0		[1. 0.]
157.4		[1.]
157.5		[1.]
157.7		[nan]
157.9		[1.]
158.0		[0.]
158.1		[nan]
158.3		[1.]
158.7		[nan]
159.0		[1.]
159.1		[1.]
159.3		[1.]
159.7		[1.]
159.9		[1.]
160.0		[ 0.  1. nan]
160.1		[1.]
160.4		[0.]
160.5		[1.]
160.6		[nan]
161.0		[ 1. nan]
161.1		[nan]
161.2		[ 1. nan]
161.5		[nan]
161.9		[ 1. nan]
162.0		[1.]
162.1		[1.]
162.2		[0.]
162.3		[1.]
162.4		[0.]
162.5		[1.]
162.9		[1.]
163.0		[1.]
163.1		[0.]
163.3		[1.]
163.4		[1.]
163.5		[0.]
163.6		[1.]
163.7		[nan]
164.3		[nan]
164.9		[1.]
165.2		[1.]
165.7		[nan]
165.8		[ 1. nan]
166.0		[1.]
166.6		[0.]
166.9		[nan]
167.1		[1.]
167.3		[1.]
168.0		[nan]
168.2		[1.]
168.5		[1.]
168.6		[1.]
169.2		[0.]
169.7		[1.]
170.0		[ 0.  1. nan]
171.2		[1.]
172.0		[nan]
173.3		[1.]
174.7		[1.]
175.6		[1.]
176.0		[1.]
176.7		[1.]
178.0		[nan]
178.3		[nan]
179.6		[0.]
180.0		[ 1. nan]
180.4		[1.]
183.5		[1.]
184.0		[1.]
184.4		[nan]
190.0		[ 1. nan]
****************************************************************************************************
Displaced	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 1.  0. nan]
****************************************************************************************************
Educational special needs	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 1.  0. nan]
****************************************************************************************************
Debtor	Tuition fees up to date
0		[ 1.  0. nan]
1		[ 0.  1. nan]
****************************************************************************************************
Tuition fees up to date	Tuition fees up to date
0.0		[0.]
1.0		[1.]
****************************************************************************************************
Gender	Tuition fees up to date
0		[ 1.  0. nan]
1		[ 0.  1. nan]
****************************************************************************************************
Scholarship holder	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 1.  0. nan]
****************************************************************************************************
Age at enrollment	Tuition fees up to date
17		[1.]
18		[ 1.  0. nan]
19		[ 0.  1. nan]
20		[ 1.  0. nan]
21		[ 1.  0. nan]
22		[ 0.  1. nan]
23		[ 1.  0. nan]
24		[ 1.  0. nan]
25		[ 1.  0. nan]
26		[ 1.  0. nan]
27		[ 1.  0. nan]
28		[ 1.  0. nan]
29		[ 1.  0. nan]
30		[ 1.  0. nan]
31		[ 1.  0. nan]
32		[ 0.  1. nan]
33		[ 1.  0. nan]
34		[ 1.  0. nan]
35		[ 1.  0. nan]
36		[ 1.  0. nan]
37		[ 0.  1. nan]
38		[ 0.  1. nan]
39		[ 1.  0. nan]
40		[ 0.  1. nan]
41		[ 0.  1. nan]
42		[ 1.  0. nan]
43		[ 1.  0. nan]
44		[ 1.  0. nan]
45		[ 1.  0. nan]
46		[ 1.  0. nan]
47		[ 1.  0. nan]
48		[ 1.  0. nan]
49		[ 1.  0. nan]
50		[ 1.  0. nan]
51		[1. 0.]
52		[ 1. nan]
53		[ 0.  1. nan]
54		[ 1.  0. nan]
55		[ 1.  0. nan]
57		[1. 0.]
58		[1.]
59		[ 0. nan]
60		[1.]
61		[1.]
62		[1.]
70		[0.]
****************************************************************************************************
International	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 0.  1. nan]
****************************************************************************************************
Curricular units 1st sem (credited)	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 1.  0. nan]
2		[ 1.  0. nan]
3		[ 1.  0. nan]
4		[ 1.  0. nan]
5		[ 1.  0. nan]
6		[ 1.  0. nan]
7		[ 1.  0. nan]
8		[ 1.  0. nan]
9		[ 1.  0. nan]
10		[ 0.  1. nan]
11		[ 0.  1. nan]
12		[0. 1.]
13		[ 1.  0. nan]
14		[ 1.  0. nan]
15		[ 1. nan]
16		[ 1. nan]
17		[1.]
18		[ 1.  0. nan]
19		[1.]
20		[1.]
****************************************************************************************************
Curricular units 1st sem (enrolled)	Tuition fees up to date
0		[ 1.  0. nan]
1		[1. 0.]
2		[ 1. nan]
3		[ 0.  1. nan]
4		[ 1.  0. nan]
5		[ 0.  1. nan]
6		[ 1.  0. nan]
7		[ 0.  1. nan]
8		[ 1.  0. nan]
9		[ 1.  0. nan]
10		[ 1.  0. nan]
11		[ 1.  0. nan]
12		[ 1.  0. nan]
13		[ 1.  0. nan]
14		[ 1. nan]
15		[ 0.  1. nan]
16		[ 1. nan]
17		[ 1.  0. nan]
18		[ 1. nan]
19		[1.]
21		[1. 0.]
23		[ 1. nan]
26		[1.]
****************************************************************************************************
Curricular units 1st sem (evaluations)	Tuition fees up to date
0		[ 1.  0. nan]
1		[1. 0.]
2		[ 1. nan]
3		[0. 1.]
4		[ 1.  0. nan]
5		[ 0.  1. nan]
6		[ 1.  0. nan]
7		[ 1.  0. nan]
8		[ 0.  1. nan]
9		[ 1.  0. nan]
10		[ 1.  0. nan]
11		[ 0.  1. nan]
12		[ 1.  0. nan]
13		[ 1.  0. nan]
14		[ 1.  0. nan]
15		[ 1.  0. nan]
16		[ 1.  0. nan]
17		[ 0.  1. nan]
18		[ 1.  0. nan]
19		[ 1.  0. nan]
20		[ 1. nan]
21		[ 1. nan]
22		[ 0.  1. nan]
23		[ 1.  0. nan]
24		[ 1.  0. nan]
25		[1.]
26		[ 1.  0. nan]
27		[ 1. nan]
28		[1.]
29		[1.]
31		[0.]
32		[nan]
33		[1.]
36		[nan]
45		[1.]
****************************************************************************************************
Curricular units 1st sem (approved)	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 1.  0. nan]
2		[ 1.  0. nan]
3		[ 0.  1. nan]
4		[ 1.  0. nan]
5		[ 1.  0. nan]
6		[ 1.  0. nan]
7		[ 1.  0. nan]
8		[ 1.  0. nan]
9		[ 1.  0. nan]
10		[ 1. nan]
11		[ 1.  0. nan]
12		[ 0.  1. nan]
13		[ 1.  0. nan]
14		[ 1.  0. nan]
15		[ 1. nan]
16		[ 1. nan]
17		[ 1. nan]
18		[ 1. nan]
19		[1.]
20		[ 1. nan]
21		[1. 0.]
26		[1.]
****************************************************************************************************
Curricular units 1st sem (grade)	Tuition fees up to date
0.0		[ 0.  1. nan]
9.8		[1.]
10.0		[ 1.  0. nan]
10.16666667		[nan]
10.2		[ 1. nan]
10.21428571		[0.]
10.25		[ 1.  0. nan]
10.28571429		[1.]
10.33333333		[ 1.  0. nan]
10.36842105		[1.]
10.375		[1.]
10.4		[ 1.  0. nan]
10.46666667		[nan]
10.5		[ 1.  0. nan]
10.57142857		[ 1. nan]
10.6		[ 0.  1. nan]
10.64285714		[1.]
10.66666667		[ 1.  0. nan]
10.69230769		[nan]
10.71428571		[1.]
10.74444444		[1.]
10.75		[ 1. nan]
10.76923077		[1.]
10.8		[ 1. nan]
10.81818182		[1.]
10.83333333		[ 1.  0. nan]
10.85714286		[1.]
10.875		[1.]
10.88888889		[1. 0.]
10.9		[1.]
10.92307692		[1.]
10.92857143		[1.]
10.98333333		[1.]
10.98571429		[1.]
11.0		[ 1.  0. nan]
11.07142857		[1.]
11.125		[1.]
11.14285714		[ 1. nan]
11.15		[1.]
11.15555556		[1.]
11.16666667		[ 1. nan]
11.2		[ 1.  0. nan]
11.23076923		[1.]
11.25		[ 1.  0. nan]
11.27272727		[nan]
11.28571429		[ 1.  0. nan]
11.29444444		[nan]
11.3		[nan]
11.325		[1.]
11.32857143		[0.]
11.33333333		[ 1.  0. nan]
11.35714286		[ 1. nan]
11.375		[1.]
11.4		[ 0.  1. nan]
11.41666667		[1.]
11.42105263		[1.]
11.42857143		[ 1.  0. nan]
11.4375		[1.]
11.44444444		[nan]
11.45454545		[ 1. nan]
11.46153846		[1.]
11.5		[ 1.  0. nan]
11.53846154		[nan]
11.54545455		[1.]
11.55555556		[1. 0.]
11.57142857		[ 1. nan]
11.575		[1.]
11.5875		[nan]
11.6		[ 1.  0. nan]
11.605		[1.]
11.61111111		[1.]
11.61428571		[nan]
11.61538462		[1.]
11.61666667		[1.]
11.625		[ 1. nan]
11.63571429		[1.]
11.64285714		[1.]
11.65833333		[1.]
11.66666667		[ 1.  0. nan]
11.67777778		[1.]
11.67857143		[1.]
11.68		[1.]
11.68333333		[1.]
11.6875		[1.]
11.69		[1.]
11.70571429		[1.]
11.71428571		[ 1.  0. nan]
11.71666667		[1.]
11.72142857		[1.]
11.72727273		[ 0. nan]
11.73333333		[1.]
11.75		[ 0.  1. nan]
11.75714286		[nan]
11.76923077		[1.]
11.7725		[1.]
11.77777778		[1.]
11.78571429		[1.]
11.8		[ 1.  0. nan]
11.81428571		[nan]
11.81666667		[nan]
11.82		[1.]
11.82857143		[1.]
11.832		[0.]
11.83333333		[ 1.  0. nan]
11.8375		[1.]
11.85		[1.]
11.85714286		[ 1. nan]
11.875		[ 1. nan]
11.88888889		[0. 1.]
11.89473684		[1.]
11.9		[ 1. nan]
11.90909091		[1.]
11.91666667		[ 0.  1. nan]
11.92307692		[0.]
11.94		[1.]
11.95		[1.]
11.96		[0.]
11.9625		[1.]
11.96666667		[1.]
11.97142857		[1.]
11.9725		[1.]
11.975		[ 1. nan]
11.98		[ 1. nan]
11.98333333		[nan]
11.98857143		[1.]
11.99444444		[1.]
12.0		[ 1.  0. nan]
12.002		[1.]
12.03571429		[nan]
12.04		[1.]
12.04142857		[1.]
12.04285714		[1.]
12.07142857		[1.]
12.07692308		[1.]
12.08285714		[nan]
12.08428571		[1.]
12.09090909		[1.]
12.1		[ 1. nan]
12.11428571		[1.]
12.12142857		[nan]
12.125		[ 1. nan]
12.13571429		[1.]
12.1375		[nan]
12.14285714		[ 0.  1. nan]
12.15384615		[ 1. nan]
12.16		[ 1.  0. nan]
12.1625		[1.]
12.16666667		[ 1.  0. nan]
12.18		[0.]
12.18333333		[ 1. nan]
12.19230769		[1.]
12.2		[ 1.  0. nan]
12.21052632		[1.]
12.21428571		[1.]
12.21714286		[1.]
12.22222222		[0. 1.]
12.22857143		[1.]
12.24285714		[1.]
12.25		[ 0.  1. nan]
12.26666667		[1.]
12.27142857		[1.]
12.28571429		[ 1.  0. nan]
12.288		[nan]
12.3		[nan]
12.32		[1.]
12.32428571		[nan]
12.32777778		[1.]
12.32857143		[1.]
12.33333333		[ 1.  0. nan]
12.34		[1.]
12.34285714		[1.]
12.35		[1.]
12.35571429		[1.]
12.35714286		[1.]
12.36		[0.]
12.36363636		[0.]
12.36571429		[1.]
12.375		[ 1. nan]
12.38571429		[1.]
12.39666667		[1.]
12.4		[ 1.  0. nan]
12.40142857		[ 1. nan]
12.4125		[1.]
12.41285714		[1.]
12.41428571		[1.]
12.415		[1.]
12.42857143		[ 1. nan]
12.44428571		[nan]
12.44444444		[1.]
12.45		[ 1. nan]
12.45428571		[1.]
12.45454545		[1.]
12.45714286		[ 1. nan]
12.46		[1.]
12.46153846		[1.]
12.46428571		[1.]
12.47142857		[ 1. nan]
12.47619048		[1.]
12.48375		[1.]
12.48571429		[1.]
12.49166667		[1.]
12.49285714		[nan]
12.49571429		[1.]
12.5		[ 1.  0. nan]
12.50625		[1.]
12.5075		[1.]
12.5125		[1.]
12.51333333		[1.]
12.51428571		[ 1. nan]
12.51666667		[1.]
12.52608696		[1.]
12.52857143		[nan]
12.5375		[1.]
12.54545455		[ 0. nan]
12.54571429		[1.]
12.55375		[1.]
12.55555556		[ 1. nan]
12.56		[0.]
12.5625		[1.]
12.565		[1.]
12.57142857		[1.]
12.57894737		[1.]
12.58		[1.]
12.58333333		[1.]
12.58571429		[nan]
12.58823529		[nan]
12.59125		[1.]
12.6		[ 1.  0. nan]
12.60625		[1.]
12.61		[1.]
12.61428571		[1.]
12.61904762		[1.]
12.62		[0. 1.]
12.625		[1.]
12.62857143		[1.]
12.63333333		[1.]
12.63636364		[ 1. nan]
12.64285714		[nan]
12.64833333		[1.]
12.65		[ 1. nan]
12.65714286		[1.]
12.66571429		[1.]
12.66666667		[ 1.  0. nan]
12.67		[nan]
12.675		[1.]
12.68421053		[1.]
12.68571429		[1.]
12.6875		[nan]
12.69230769		[1.]
12.69428571		[nan]
12.7		[ 1. nan]
12.71428571		[1.]
12.71857143		[1.]
12.725		[1.]
12.72727273		[1. 0.]
12.72857143		[1.]
12.73333333		[1.]
12.74		[nan]
12.74285714		[nan]
12.74428571		[1.]
12.75		[ 1.  0. nan]
12.75714286		[1.]
12.75833333		[0.]
12.76428571		[1.]
12.76571429		[nan]
12.76666667		[ 1. nan]
12.77125		[1.]
12.77142857		[1.]
12.77777778		[1.]
12.78333333		[1.]
12.78571429		[1.]
12.79142857		[1.]
12.8		[ 1.  0. nan]
12.80833333		[1.]
12.81666667		[1.]
12.81818182		[1.]
12.82857143		[ 1. nan]
12.82875		[1.]
12.83333333		[ 1. nan]
12.83571429		[nan]
12.8375		[1.]
12.84142857		[1.]
12.84615385		[1.]
12.84714286		[1.]
12.85		[1.]
12.85555556		[1.]
12.85714286		[ 1. nan]
12.858		[1.]
12.86		[ 0.  1. nan]
12.875		[1.]
12.87857143		[1.]
12.88625		[1.]
12.8875		[ 1. nan]
12.88888889		[1.]
12.9		[ 1. nan]
12.90142857		[nan]
12.90857143		[1.]
12.90909091		[nan]
12.91		[1.]
12.91428571		[nan]
12.91666667		[ 1. nan]
12.92875		[nan]
12.93333333		[1.]
12.93375		[1.]
12.93875		[1.]
12.94		[1. 0.]
12.94428571		[1.]
12.95		[ 1. nan]
12.96666667		[1.]
12.97166667		[1.]
12.98125		[1.]
12.98285714		[1.]
12.99333333		[1.]
13.0		[ 1.  0. nan]
13.01125		[1.]
13.01666667		[1.]
13.02428571		[1.]
13.025		[1.]
13.03		[1.]
13.03333333		[nan]
13.04		[1.]
13.04444444		[1.]
13.045		[1.]
13.04666667		[1.]
13.05		[1.]
13.05375		[nan]
13.05555556		[1.]
13.05857143		[nan]
13.05875		[1.]
13.05882353		[nan]
13.06666667		[1.]
13.07142857		[1.]
13.07375		[1.]
13.075		[ 1. nan]
13.07571429		[nan]
13.08		[1.]
13.08166667		[1.]
13.08714286		[1.]
13.09090909		[1.]
13.09428571		[1.]
13.1		[ 1. nan]
13.105		[1.]
13.10571429		[1.]
13.11		[1.]
13.11111111		[1.]
13.11625		[1.]
13.11666667		[1.]
13.11714286		[nan]
13.125		[ 0.  1. nan]
13.126		[1.]
13.128		[1.]
13.13333333		[1.]
13.135		[1.]
13.14285714		[ 1. nan]
13.15		[1.]
13.15142857		[nan]
13.15384615		[1.]
13.15428571		[1.]
13.16666667		[ 1.  0. nan]
13.175		[1.]
13.17571429		[1.]
13.17647059		[1.]
13.18181818		[1.]
13.18333333		[1.]
13.188		[1.]
13.19		[1.]
13.19857143		[1.]
13.2		[ 1.  0. nan]
13.21428571		[1.]
13.22		[nan]
13.22222222		[1.]
13.2325		[1.]
13.2375		[1.]
13.24285714		[1.]
13.24875		[nan]
13.25		[ 1.  0. nan]
13.25375		[nan]
13.2575		[1.]
13.25857143		[1.]
13.26		[1.]
13.26666667		[1.]
13.27142857		[1.]
13.27166667		[1.]
13.27272727		[1.]
13.27625		[1.]
13.28571429		[ 1.  0. nan]
13.29		[1.]
13.29166667		[1.]
13.29666667		[1.]
13.29714286		[1.]
13.3		[ 1. nan]
13.30625		[1.]
13.30769231		[nan]
13.30857143		[1.]
13.31		[nan]
13.3125		[1.]
13.31428571		[1.]
13.32714286		[0.]
13.32857143		[1.]
13.33142857		[1.]
13.33333333		[ 0.  1. nan]
13.336		[1.]
13.34		[ 1. nan]
13.3425		[nan]
13.34285714		[nan]
13.345		[1.]
13.3475		[1.]
13.35		[1.]
13.3625		[1.]
13.3675		[1.]
13.37		[1.]
13.37142857		[1.]
13.375		[1. 0.]
13.38125		[nan]
13.38142857		[nan]
13.38333333		[1.]
13.38461538		[0.]
13.385		[nan]
13.38571429		[1.]
13.3875		[1.]
13.38888889		[nan]
13.39		[1.]
13.39125		[nan]
13.39166667		[nan]
13.39473684		[1.]
13.398		[1.]
13.4		[ 1.  0. nan]
13.40375		[1.]
13.41142857		[1.]
13.41428571		[nan]
13.41625		[1.]
13.41666667		[1.]
13.4173913		[1.]
13.42222222		[1.]
13.42375		[1.]
13.4275		[nan]
13.42857143		[ 1.  0. nan]
13.43333333		[nan]
13.435		[nan]
13.44		[1.]
13.44285714		[ 1. nan]
13.44444444		[ 1. nan]
13.45		[1.]
13.45454545		[1.]
13.45714286		[1.]
13.46		[1.]
13.46285714		[ 0. nan]
13.46428571		[1.]
13.46666667		[1.]
13.46714286		[1.]
13.46875		[1.]
13.47		[1.]
13.47142857		[1.]
13.48333333		[1.]
13.48571429		[1.]
13.48714286		[nan]
13.49285714		[nan]
13.49666667		[1.]
13.5		[ 1.  0. nan]
13.50714286		[1.]
13.51285714		[nan]
13.51428571		[1.]
13.51666667		[1.]
13.52857143		[nan]
13.53333333		[1.]
13.54285714		[1.]
13.54545455		[nan]
13.54833333		[1.]
13.55		[1.]
13.55555556		[1.]
13.55714286		[ 1. nan]
13.56875		[1.]
13.57142857		[ 1.  0. nan]
13.57625		[1.]
13.58		[1.]
13.58333333		[nan]
13.58571429		[1.]
13.59833333		[1.]
13.6		[ 1.  0. nan]
13.605		[1.]
13.6125		[1.]
13.61428571		[ 1. nan]
13.61538462		[nan]
13.625		[ 1. nan]
13.62857143		[1.]
13.63		[1.]
13.6375		[nan]
13.64285714		[1.]
13.66666667		[ 1.  0. nan]
13.67		[1.]
13.67142857		[nan]
13.67714286		[1.]
13.68142857		[1.]
13.68333333		[1.]
13.68571429		[nan]
13.6875		[1.]
13.69428571		[nan]
13.7		[ 0.  1. nan]
13.71428571		[ 1. nan]
13.72571429		[nan]
13.72727273		[1.]
13.73125		[1.]
13.73166667		[1.]
13.73684211		[1.]
13.74166667		[nan]
13.74285714		[1.]
13.74625		[1.]
13.75		[ 1.  0. nan]
13.755		[1.]
13.76111111		[1.]
13.76125		[nan]
13.76375		[1.]
13.76666667		[1.]
13.76923077		[1.]
13.77571429		[1.]
13.77857143		[1.]
13.78		[1.]
13.78125		[1.]
13.78571429		[ 1. nan]
13.79666667		[1.]
13.79714286		[nan]
13.79857143		[1.]
13.8		[ 1. nan]
13.80142857		[1.]
13.805		[1.]
13.81375		[1.]
13.81428571		[nan]
13.81714286		[1.]
13.81818182		[ 1. nan]
13.82857143		[1.]
13.83333333		[ 1.  0. nan]
13.84285714		[1.]
13.84615385		[ 1. nan]
13.85		[nan]
13.85714286		[ 1. nan]
13.86166667		[1.]
13.86333333		[1.]
13.86714286		[1.]
13.875		[1.]
13.87571429		[1.]
13.87714286		[1.]
13.88125		[1.]
13.88428571		[nan]
13.8875		[nan]
13.89428571		[1.]
13.9		[1.]
13.91428571		[1.]
13.91666667		[nan]
13.91857143		[1.]
13.92307692		[1.]
13.92571429		[1.]
13.928		[1.]
13.93714286		[1.]
13.9475		[1.]
13.95714286		[1.]
13.95833333		[1.]
13.96666667		[ 1. nan]
13.97142857		[1.]
13.97666667		[1.]
13.98		[nan]
13.98333333		[1.]
13.99571429		[1.]
14.0		[ 1.  0. nan]
14.00375		[nan]
14.01428571		[1.]
14.01666667		[1.]
14.03333333		[nan]
14.035		[1.]
14.05		[1.]
14.05714286		[1.]
14.06		[1.]
14.06666667		[1.]
14.07125		[1.]
14.07142857		[1.]
14.07222222		[1.]
14.08571429		[nan]
14.09333333		[1.]
14.1		[1.]
14.10571429		[1.]
14.11		[1.]
14.11111111		[1.]
14.11428571		[nan]
14.11571429		[1.]
14.125		[ 1. nan]
14.12571429		[1.]
14.13125		[nan]
14.13714286		[1.]
14.14125		[1.]
14.14285714		[ 1.  0. nan]
14.14571429		[1.]
14.15		[1.]
14.15375		[1.]
14.15384615		[nan]
14.16666667		[ 1. nan]
14.175		[nan]
14.2		[ 1.  0. nan]
14.20857143		[1.]
14.21428571		[1.]
14.22222222		[nan]
14.23285714		[1.]
14.23333333		[1.]
14.24		[1.]
14.24444444		[1.]
14.25		[ 1. nan]
14.2525		[1.]
14.25714286		[1.]
14.26666667		[1.]
14.26875		[1.]
14.27272727		[nan]
14.28333333		[1.]
14.28571429		[1.]
14.3		[ 1. nan]
14.30714286		[1.]
14.30769231		[1.]
14.31285714		[1.]
14.31428571		[ 1. nan]
14.31666667		[1.]
14.32857143		[1.]
14.33333333		[ 1.  0. nan]
14.34125		[1.]
14.35		[1.]
14.35555556		[1.]
14.35714286		[1.]
14.36		[nan]
14.368		[1.]
14.37		[1.]
14.375		[1.]
14.3775		[1.]
14.38461538		[1.]
14.4		[ 1.  0. nan]
14.4025		[1.]
14.40285714		[nan]
14.4125		[nan]
14.41428571		[1.]
14.42428571		[1.]
14.42857143		[ 1.  0. nan]
14.44142857		[1.]
14.44444444		[1.]
14.445		[1.]
14.45		[1.]
14.45454545		[1.]
14.455		[1.]
14.46		[1.]
14.48571429		[1.]
14.5		[ 1.  0. nan]
14.50125		[1.]
14.51428571		[nan]
14.52857143		[1.]
14.53571429		[1.]
14.53714286		[1.]
14.53846154		[1.]
14.54		[1.]
14.54285714		[nan]
14.56571429		[1.]
14.56666667		[1.]
14.57142857		[1.]
14.57285714		[1.]
14.575		[1.]
14.58333333		[nan]
14.59444444		[1.]
14.6		[1. 0.]
14.60428571		[1.]
14.61538462		[1.]
14.61666667		[1.]
14.625		[1.]
14.63157895		[1.]
14.63636364		[1.]
14.65		[1.]
14.65285714		[1.]
14.65428571		[nan]
14.6625		[1.]
14.66666667		[ 0.  1. nan]
14.668		[1.]
14.68285714		[1.]
14.69285714		[1.]
14.69625		[1.]
14.7		[ 1. nan]
14.70625		[nan]
14.71111111		[1.]
14.71428571		[ 1. nan]
14.71666667		[1.]
14.72571429		[nan]
14.72727273		[ 1. nan]
14.72857143		[1.]
14.73142857		[nan]
14.75		[ 0.  1. nan]
14.75714286		[ 1. nan]
14.77777778		[nan]
14.7875		[1.]
14.8		[1.]
14.80571429		[1.]
14.82857143		[nan]
14.83333333		[ 1.  0. nan]
14.83857143		[1.]
14.84285714		[1.]
14.85		[ 1. nan]
14.85714286		[ 1. nan]
14.86857143		[1.]
14.875		[1.]
14.88875		[1.]
14.88888889		[nan]
14.90142857		[1.]
14.9125		[1.]
14.91875		[1.]
14.93333333		[1.]
14.935		[1.]
14.93571429		[nan]
14.95		[1.]
14.96285714		[1.]
14.98333333		[1.]
14.98571429		[1.]
14.99285714		[1.]
15.0		[ 1.  0. nan]
15.01428571		[1.]
15.02857143		[1.]
15.05263158		[1.]
15.07		[1.]
15.07571429		[1.]
15.07777778		[1.]
15.08333333		[nan]
15.09090909		[1. 0.]
15.10142857		[1.]
15.11		[1.]
15.11428571		[1.]
15.115		[1.]
15.11666667		[nan]
15.125		[ 1.  0. nan]
15.13571429		[nan]
15.14285714		[1.]
15.145		[nan]
15.15555556		[1.]
15.16666667		[ 1. nan]
15.2		[ 1.  0. nan]
15.22857143		[1.]
15.23076923		[nan]
15.25		[1. 0.]
15.28571429		[ 1. nan]
15.3		[1.]
15.30769231		[ 1. nan]
15.33333333		[ 1. nan]
15.375		[1.]
15.4		[ 1. nan]
15.42857143		[0. 1.]
15.43333333		[1.]
15.44444444		[1.]
15.46875		[1.]
15.5		[1.]
15.53846154		[1.]
15.57		[nan]
15.57142857		[ 1. nan]
15.625		[nan]
15.63375		[nan]
15.65714286		[1.]
15.66		[nan]
15.66666667		[ 1. nan]
15.67571429		[1.]
15.69230769		[1.]
15.7		[1.]
15.71428571		[ 1. nan]
15.75		[1.]
15.79857143		[1.]
15.8		[ 1. nan]
15.83333333		[1.]
15.85714286		[1.]
15.88571429		[nan]
15.88888889		[1.]
15.94285714		[1.]
15.95714286		[1.]
16.0		[ 1.  0. nan]
16.11111111		[1.]
16.13142857		[1.]
16.14285714		[ 1. nan]
16.15384615		[nan]
16.16666667		[0. 1.]
16.2		[ 1. nan]
16.33333333		[0. 1.]
16.4		[ 1. nan]
16.42857143		[1.]
16.46153846		[1.]
16.5		[1. 0.]
16.57142857		[nan]
16.71428571		[1.]
16.83333333		[1.]
16.85714286		[1.]
16.88571429		[1.]
16.9		[1.]
17.0		[1.]
17.00555556		[1.]
17.11111111		[1.]
17.125		[1.]
17.33333333		[1.]
18.0		[ 1. nan]
18.875		[nan]
****************************************************************************************************
Curricular units 1st sem (without evaluations)	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 1.  0. nan]
2		[ 1.  0. nan]
3		[ 1.  0. nan]
4		[ 1.  0. nan]
5		[1.]
6		[0. 1.]
7		[ 1.  0. nan]
8		[ 1.  0. nan]
10		[1.]
12		[1.]
****************************************************************************************************
Curricular units 2nd sem (credited)	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 1.  0. nan]
2		[ 1.  0. nan]
3		[ 1.  0. nan]
4		[ 1.  0. nan]
5		[ 1.  0. nan]
6		[ 1.  0. nan]
7		[ 1. nan]
8		[ 0.  1. nan]
9		[ 0.  1. nan]
10		[ 1.  0. nan]
11		[ 1. nan]
12		[ 1.  0. nan]
13		[ 1.  0. nan]
14		[ 1. nan]
15		[ 1. nan]
16		[ 1. nan]
18		[ 1. nan]
19		[1.]
****************************************************************************************************
Curricular units 2nd sem (enrolled)	Tuition fees up to date
0		[ 1.  0. nan]
1		[1.]
2		[ 0.  1. nan]
3		[ 1. nan]
4		[ 0.  1. nan]
5		[ 0.  1. nan]
6		[ 1.  0. nan]
7		[ 1.  0. nan]
8		[ 0.  1. nan]
9		[ 1.  0. nan]
10		[ 1.  0. nan]
11		[ 1.  0. nan]
12		[ 1.  0. nan]
13		[ 1.  0. nan]
14		[ 0.  1. nan]
15		[ 1. nan]
16		[0.]
17		[ 1. nan]
18		[1.]
19		[1.]
21		[1.]
23		[ 1. nan]
****************************************************************************************************
Curricular units 2nd sem (evaluations)	Tuition fees up to date
0		[ 1.  0. nan]
1		[1.]
2		[ 0. nan]
3		[ 1. nan]
4		[ 0.  1. nan]
5		[ 0.  1. nan]
6		[ 1.  0. nan]
7		[ 1.  0. nan]
8		[ 0.  1. nan]
9		[ 0.  1. nan]
10		[ 1.  0. nan]
11		[ 1.  0. nan]
12		[ 1.  0. nan]
13		[ 1.  0. nan]
14		[ 1.  0. nan]
15		[ 1.  0. nan]
16		[ 0.  1. nan]
17		[ 1.  0. nan]
18		[ 1.  0. nan]
19		[ 1.  0. nan]
20		[ 1.  0. nan]
21		[1. 0.]
22		[ 1. nan]
23		[ 1. nan]
24		[1. 0.]
25		[1.]
26		[1.]
27		[1.]
28		[nan]
33		[1.]
****************************************************************************************************
Curricular units 2nd sem (approved)	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 0.  1. nan]
2		[ 1.  0. nan]
3		[ 1.  0. nan]
4		[ 1.  0. nan]
5		[ 1.  0. nan]
6		[ 1.  0. nan]
7		[ 1.  0. nan]
8		[ 0.  1. nan]
9		[ 0.  1. nan]
10		[ 1.  0. nan]
11		[ 1.  0. nan]
12		[ 1.  0. nan]
13		[ 1.  0. nan]
14		[ 1. nan]
16		[1.]
17		[ 1. nan]
18		[1.]
19		[1.]
20		[ 1. nan]
****************************************************************************************************
Curricular units 2nd sem (grade)	Tuition fees up to date
0.0		[ 0.  1. nan]
10.0		[ 0.  1. nan]
10.16666667		[1. 0.]
10.2		[1.]
10.25		[ 1.  0. nan]
10.33333333		[ 1.  0. nan]
10.375		[1.]
10.4		[ 1. nan]
10.42857143		[1. 0.]
10.44444444		[ 1. nan]
10.5		[ 1.  0. nan]
10.57142857		[ 1.  0. nan]
10.6		[ 1.  0. nan]
10.66666667		[ 0.  1. nan]
10.71428571		[1.]
10.72727273		[1.]
10.75		[ 1.  0. nan]
10.8		[ 0.  1. nan]
10.83333333		[ 1.  0. nan]
10.84		[nan]
10.85714286		[ 1.  0. nan]
10.868		[1.]
10.92307692		[nan]
10.97857143		[nan]
10.98333333		[1.]
11.0		[ 1.  0. nan]
11.07692308		[1.]
11.08333333		[0.]
11.1		[ 1. nan]
11.11111111		[nan]
11.125		[1.]
11.14285714		[ 1. nan]
11.16666667		[ 1. nan]
11.18181818		[ 1. nan]
11.2		[ 0.  1. nan]
11.22222222		[1.]
11.225		[1.]
11.24285714		[1.]
11.25		[ 1.  0. nan]
11.2625		[1.]
11.28571429		[1.]
11.3		[nan]
11.325		[1.]
11.32857143		[0.]
11.33333333		[ 1.  0. nan]
11.34		[0.]
11.34714286		[nan]
11.35714286		[nan]
11.36363636		[1. 0.]
11.375		[1.]
11.38461538		[1. 0.]
11.4		[ 1.  0. nan]
11.425		[1.]
11.42857143		[ 0.  1. nan]
11.4375		[1.]
11.44444444		[ 1. nan]
11.45454545		[1.]
11.46153846		[1.]
11.4625		[nan]
11.5		[ 1.  0. nan]
11.50625		[nan]
11.54545455		[1.]
11.55		[1.]
11.55555556		[1.]
11.56666667		[0.]
11.57142857		[ 1. nan]
11.575		[0.]
11.58		[0.]
11.58333333		[1.]
11.6		[ 1.  0. nan]
11.61538462		[1.]
11.625		[ 1. nan]
11.63571429		[1.]
11.63636364		[1.]
11.64285714		[1.]
11.65		[nan]
11.66666667		[ 1.  0. nan]
11.675		[1.]
11.68		[1.]
11.68333333		[1.]
11.68571429		[1.]
11.69		[1.]
11.7		[1.]
11.70571429		[1.]
11.70833333		[1.]
11.7125		[nan]
11.71428571		[ 1. nan]
11.72142857		[1.]
11.725		[nan]
11.736		[1.]
11.7375		[1.]
11.75		[ 1.  0. nan]
11.77142857		[1.]
11.77777778		[ 1. nan]
11.78333333		[1.]
11.78571429		[1.]
11.8		[ 1.  0. nan]
11.80833333		[1.]
11.8125		[0.]
11.82		[1.]
11.832		[0.]
11.83333333		[ 1.  0. nan]
11.8375		[1.]
11.85625		[1.]
11.85714286		[ 1. nan]
11.875		[ 1. nan]
11.88888889		[1.]
11.9		[1.]
11.90909091		[ 1. nan]
11.92857143		[1.]
11.93333333		[1.]
11.95		[1.]
11.95625		[1.]
11.9625		[1.]
11.96666667		[1.]
11.97142857		[1.]
11.98		[1.]
11.98857143		[1.]
12.0		[ 1.  0. nan]
12.002		[1.]
12.04142857		[1.]
12.04166667		[1.]
12.04285714		[1.]
12.05		[1.]
12.05555556		[1.]
12.06375		[1.]
12.06666667		[1.]
12.07142857		[1.]
12.08285714		[nan]
12.08333333		[ 1. nan]
12.08428571		[1.]
12.09090909		[1.]
12.10526316		[1.]
12.11428571		[1.]
12.11666667		[1.]
12.11764706		[1.]
12.11875		[1.]
12.125		[1.]
12.12857143		[nan]
12.14		[nan]
12.14285714		[ 1. nan]
12.15		[1.]
12.15714286		[1.]
12.16		[ 1. nan]
12.16666667		[ 1.  0. nan]
12.17647059		[nan]
12.18181818		[ 1. nan]
12.18333333		[nan]
12.18888889		[1.]
12.19230769		[1.]
12.19285714		[nan]
12.2		[ 1. nan]
12.20555556		[1.]
12.21052632		[1.]
12.21428571		[1.]
12.22142857		[1.]
12.22222222		[0. 1.]
12.22857143		[1.]
12.23333333		[1.]
12.2375		[1.]
12.24285714		[1.]
12.25		[ 0.  1. nan]
12.27272727		[ 1. nan]
12.28125		[nan]
12.28571429		[ 1.  0. nan]
12.288		[nan]
12.3		[ 1. nan]
12.30769231		[ 1. nan]
12.32		[1.]
12.32142857		[1.]
12.32428571		[nan]
12.32857143		[1.]
12.33		[1.]
12.33125		[1.]
12.33333333		[ 1.  0. nan]
12.34285714		[1.]
12.35571429		[1.]
12.35714286		[1.]
12.36111111		[nan]
12.36363636		[nan]
12.36571429		[1.]
12.375		[ 1. nan]
12.38		[1.]
12.38461538		[1.]
12.38571429		[1.]
12.4		[ 0.  1. nan]
12.40142857		[nan]
12.4125		[1.]
12.41285714		[1.]
12.41428571		[1.]
12.415		[1.]
12.41666667		[ 1. nan]
12.42		[1.]
12.42857143		[ 1. nan]
12.43142857		[1.]
12.44555556		[1.]
12.45		[nan]
12.45428571		[1.]
12.45714286		[nan]
12.46		[1.]
12.46153846		[1.]
12.46428571		[1.]
12.47142857		[1.]
12.48125		[1.]
12.48375		[1.]
12.48571429		[1.]
12.4875		[nan]
12.49166667		[1.]
12.5		[ 1.  0. nan]
12.5075		[1.]
12.5125		[1.]
12.51428571		[1.]
12.51666667		[1.]
12.525		[1.]
12.52608696		[1.]
12.54545455		[1.]
12.54571429		[1.]
12.55		[1.]
12.55375		[1.]
12.55555556		[1.]
12.56		[0.]
12.5625		[1.]
12.57142857		[ 1. nan]
12.575		[1.]
12.58333333		[1.]
12.6		[ 1.  0. nan]
12.61		[1.]
12.6125		[1.]
12.61428571		[1.]
12.61666667		[1.]
12.62142857		[1.]
12.625		[ 1. nan]
12.62857143		[1.]
12.63333333		[1.]
12.63636364		[1.]
12.6375		[1.]
12.64285714		[nan]
12.65125		[1.]
12.65714286		[1.]
12.66571429		[1.]
12.66666667		[ 1.  0. nan]
12.66875		[1.]
12.67142857		[1.]
12.675		[1.]
12.68571429		[1.]
12.6875		[1.]
12.68888889		[nan]
12.69428571		[nan]
12.7		[1. 0.]
12.71428571		[ 1. nan]
12.72727273		[1.]
12.72857143		[1.]
12.73333333		[1.]
12.74285714		[nan]
12.74428571		[1.]
12.75		[ 1.  0. nan]
12.75714286		[1.]
12.75833333		[0.]
12.76111111		[1.]
12.76428571		[1.]
12.76666667		[ 1. nan]
12.77125		[1.]
12.77142857		[1.]
12.77777778		[ 1. nan]
12.78571429		[1.]
12.78947368		[1.]
12.79142857		[1.]
12.8		[ 1. nan]
12.80555556		[1.]
12.81428571		[1.]
12.81818182		[ 1. nan]
12.82		[1.]
12.82375		[1.]
12.82571429		[1.]
12.82857143		[nan]
12.82875		[1.]
12.83333333		[ 1.  0. nan]
12.84142857		[1.]
12.84285714		[1.]
12.84615385		[ 1. nan]
12.85714286		[ 1. nan]
12.858		[1.]
12.86		[ 1. nan]
12.875		[0. 1.]
12.88		[0.]
12.88625		[1.]
12.8875		[ 1. nan]
12.88888889		[1.]
12.89285714		[1.]
12.9		[1.]
12.90857143		[1.]
12.90909091		[1.]
12.91		[1.]
12.91111111		[nan]
12.9125		[1.]
12.91428571		[nan]
12.91666667		[ 1. nan]
12.92307692		[1.]
12.93375		[1.]
12.93625		[1.]
12.9375		[1.]
12.93875		[1.]
12.94285714		[1.]
12.94375		[1.]
12.94428571		[1.]
12.95		[1.]
12.96666667		[1.]
12.96818182		[1.]
12.98125		[1.]
13.0		[ 1.  0. nan]
13.01125		[1.]
13.01666667		[1.]
13.01875		[1.]
13.02428571		[1.]
13.03125		[nan]
13.03333333		[nan]
13.0375		[1.]
13.04285714		[1.]
13.04444444		[1.]
13.045		[1.]
13.05		[1.]
13.05375		[nan]
13.05857143		[nan]
13.05875		[1.]
13.0625		[1.]
13.06363636		[1.]
13.06666667		[1.]
13.07142857		[1.]
13.07375		[1.]
13.07571429		[nan]
13.08		[1.]
13.08333333		[nan]
13.08571429		[1.]
13.09090909		[1.]
13.09444444		[nan]
13.1		[ 1. nan]
13.105		[1.]
13.10571429		[1.]
13.11		[1.]
13.11111111		[1.]
13.11625		[1.]
13.11666667		[1.]
13.12222222		[1.]
13.125		[ 1.  0. nan]
13.12555556		[1.]
13.126		[1.]
13.12857143		[nan]
13.1375		[nan]
13.14285714		[1.]
13.15		[1.]
13.15142857		[nan]
13.15428571		[1.]
13.16428571		[0.]
13.16666667		[ 1.  0. nan]
13.18181818		[1.]
13.18333333		[1.]
13.19333333		[1.]
13.2		[ 1.  0. nan]
13.20714286		[1.]
13.2125		[1.]
13.21428571		[1.]
13.21875		[1.]
13.22		[nan]
13.22222222		[1.]
13.225		[nan]
13.2325		[1.]
13.24285714		[1.]
13.24444444		[1.]
13.24875		[nan]
13.25		[ 1.  0. nan]
13.25375		[nan]
13.2575		[1.]
13.25857143		[1.]
13.26666667		[ 1. nan]
13.27625		[1.]
13.28571429		[ 1. nan]
13.29		[1.]
13.29166667		[1.]
13.29444444		[1.]
13.29714286		[1.]
13.3		[ 1. nan]
13.30625		[1.]
13.31		[nan]
13.3125		[ 1. nan]
13.31375		[1.]
13.31428571		[1.]
13.32625		[1.]
13.32714286		[0.]
13.32857143		[1.]
13.33142857		[1.]
13.33333333		[ 1.  0. nan]
13.3425		[nan]
13.345		[1.]
13.3475		[1.]
13.35		[1.]
13.35714286		[nan]
13.3675		[1.]
13.375		[1.]
13.38125		[nan]
13.38142857		[nan]
13.38333333		[1.]
13.385		[nan]
13.3875		[1.]
13.39125		[nan]
13.4		[ 1.  0. nan]
13.40375		[1.]
13.41111111		[1.]
13.41428571		[nan]
13.41625		[1.]
13.41666667		[1.]
13.4173913		[1.]
13.42375		[1.]
13.4275		[nan]
13.42857143		[ 1. nan]
13.435		[nan]
13.44285714		[ 1. nan]
13.45		[1.]
13.45454545		[nan]
13.46153846		[1.]
13.472		[1.]
13.475		[1.]
13.48333333		[1.]
13.48571429		[1.]
13.48714286		[nan]
13.49375		[1.]
13.49666667		[1.]
13.5		[ 1.  0. nan]
13.51285714		[nan]
13.51428571		[1.]
13.51666667		[1.]
13.53333333		[1.]
13.5425		[1.]
13.54285714		[ 1. nan]
13.54444444		[nan]
13.54545455		[1.]
13.55		[ 1. nan]
13.55473684		[1.]
13.55555556		[nan]
13.55714286		[nan]
13.5625		[1.]
13.57142857		[ 1.  0. nan]
13.57625		[1.]
13.58333333		[ 1. nan]
13.58571429		[1.]
13.595		[1.]
13.6		[ 1.  0. nan]
13.61428571		[ 1. nan]
13.61538462		[1.]
13.625		[1.]
13.62777778		[1.]
13.63		[1.]
13.64285714		[1.]
13.65		[1.]
13.65625		[1.]
13.65714286		[1.]
13.661		[1.]
13.6625		[1.]
13.66666667		[ 1.  0. nan]
13.67		[1.]
13.67142857		[nan]
13.68142857		[1.]
13.68333333		[1.]
13.7		[1.]
13.7125		[1.]
13.71428571		[ 1. nan]
13.71666667		[1.]
13.72727273		[nan]
13.73125		[1.]
13.74285714		[1.]
13.74444444		[1.]
13.74625		[1.]
13.75		[ 1.  0. nan]
13.75555556		[1.]
13.76		[1.]
13.76125		[nan]
13.76375		[1.]
13.77142857		[1.]
13.775		[ 1. nan]
13.77777778		[1.]
13.78		[1.]
13.78125		[1.]
13.78888889		[1.]
13.79857143		[1.]
13.8		[ 1.  0. nan]
13.81111111		[1.]
13.81285714		[nan]
13.81375		[1.]
13.81428571		[nan]
13.81714286		[1.]
13.82		[1.]
13.825		[1.]
13.82857143		[1.]
13.83333333		[ 1. nan]
13.8375		[1.]
13.84285714		[1.]
13.85714286		[ 1. nan]
13.86714286		[1.]
13.875		[1.]
13.88125		[1.]
13.8875		[1.]
13.88888889		[1.]
13.9		[1.]
13.90625		[nan]
13.91		[1.]
13.9125		[1.]
13.91666667		[nan]
13.92571429		[1.]
13.92727273		[1.]
13.93714286		[1.]
13.9375		[1.]
13.94444444		[1.]
13.9475		[1.]
13.95714286		[1.]
13.96666667		[ 1. nan]
13.97142857		[1.]
13.98		[nan]
13.98333333		[1.]
13.98375		[nan]
13.99444444		[1.]
14.0		[ 1.  0. nan]
14.01428571		[1.]
14.01666667		[1.]
14.02222222		[nan]
14.02625		[1.]
14.03333333		[nan]
14.035		[1.]
14.0375		[nan]
14.0475		[1.]
14.05142857		[1.]
14.05555556		[nan]
14.06		[1.]
14.0625		[ 1. nan]
14.06666667		[1.]
14.07125		[1.]
14.07142857		[nan]
14.07777778		[nan]
14.08571429		[nan]
14.09090909		[nan]
14.1		[ 1. nan]
14.10625		[1.]
14.11		[1.]
14.11428571		[1.]
14.11571429		[1.]
14.125		[ 1. nan]
14.13714286		[1.]
14.14125		[1.]
14.14285714		[ 1. nan]
14.14444444		[1.]
14.15		[1.]
14.15375		[1.]
14.16666667		[ 1.  0. nan]
14.17857143		[1.]
14.1875		[1.]
14.2		[ 1.  0. nan]
14.2125		[1.]
14.21428571		[1.]
14.22222222		[1.]
14.23076923		[1.]
14.23125		[ 1. nan]
14.23285714		[1.]
14.23333333		[1.]
14.24		[1.]
14.25		[ 1.  0. nan]
14.2525		[1.]
14.25714286		[1.]
14.26666667		[1.]
14.26875		[1.]
14.2725		[1.]
14.28571429		[ 1.  0. nan]
14.28625		[1.]
14.3		[nan]
14.31285714		[1.]
14.31428571		[ 1. nan]
14.31666667		[1.]
14.32857143		[1.]
14.33333333		[ 1. nan]
14.34125		[1.]
14.34285714		[1.]
14.345		[1.]
14.35		[1.]
14.35555556		[nan]
14.35714286		[1.]
14.36		[nan]
14.3625		[1.]
14.36875		[1.]
14.375		[1.]
14.3775		[1.]
14.3875		[ 1. nan]
14.38888889		[1.]
14.4		[ 1. nan]
14.4025		[1.]
14.40285714		[nan]
14.41428571		[1.]
14.42857143		[ 1. nan]
14.4375		[1.]
14.44444444		[1.]
14.445		[1.]
14.44875		[1.]
14.46153846		[1.]
14.4625		[1.]
14.47142857		[1.]
14.4775		[nan]
14.47777778		[1.]
14.5		[ 1.  0. nan]
14.50125		[1.]
14.51111111		[1.]
14.51125		[1.]
14.5125		[1.]
14.51428571		[nan]
14.53333333		[1.]
14.53571429		[1.]
14.53714286		[1.]
14.53846154		[1.]
14.54		[1.]
14.545		[1.]
14.55555556		[1.]
14.56571429		[1.]
14.56666667		[1.]
14.57142857		[ 1. nan]
14.575		[1.]
14.58333333		[1.]
14.58888889		[1.]
14.6		[ 1. nan]
14.61666667		[1.]
14.62222222		[1.]
14.625		[0. 1.]
14.62777778		[1.]
14.63375		[1.]
14.63636364		[nan]
14.65		[1.]
14.65285714		[1.]
14.65428571		[nan]
14.65555556		[nan]
14.6625		[1.]
14.66666667		[ 0.  1. nan]
14.67142857		[1.]
14.6875		[1.]
14.7		[nan]
14.71428571		[ 1. nan]
14.71666667		[1.]
14.71875		[1.]
14.72571429		[nan]
14.73142857		[nan]
14.7375		[1.]
14.75		[ 1. nan]
14.75555556		[1.]
14.75625		[1.]
14.77272727		[1.]
14.775		[1.]
14.78888889		[1.]
14.8		[ 1.  0. nan]
14.80571429		[1.]
14.8125		[1.]
14.81818182		[nan]
14.82857143		[nan]
14.83333333		[ 1. nan]
14.85		[1.]
14.85714286		[ 1. nan]
14.86363636		[1.]
14.86444444		[nan]
14.875		[1.]
14.88181818		[nan]
14.88875		[1.]
14.88888889		[ 1. nan]
14.9		[1.]
14.90142857		[1.]
14.9125		[nan]
14.925		[1.]
14.93333333		[1.]
14.935		[1.]
14.93571429		[nan]
14.95555556		[1.]
14.9625		[1.]
14.96285714		[1.]
14.98333333		[1.]
14.98571429		[1.]
14.99285714		[1.]
15.0		[ 1.  0. nan]
15.01111111		[ 1. nan]
15.0125		[1.]
15.01818182		[1.]
15.02352941		[1.]
15.02857143		[1.]
15.05555556		[1.]
15.0625		[nan]
15.06875		[1.]
15.07		[1.]
15.075		[nan]
15.07571429		[1.]
15.092		[1.]
15.1		[1.]
15.11		[1.]
15.11111111		[1.]
15.11428571		[1.]
15.125		[1.]
15.12777778		[1.]
15.14285714		[ 1. nan]
15.1625		[1.]
15.16666667		[ 1.  0. nan]
15.18888889		[1.]
15.2		[ 1. nan]
15.22857143		[1.]
15.23076923		[1.]
15.25		[1.]
15.28571429		[ 1. nan]
15.2875		[nan]
15.3		[ 1. nan]
15.30769231		[1.]
15.3125		[1.]
15.33333333		[1.]
15.35		[1.]
15.355		[1.]
15.36666667		[1.]
15.3875		[nan]
15.4		[ 1. nan]
15.42857143		[nan]
15.43333333		[1.]
15.5		[ 1. nan]
15.52222222		[1.]
15.55555556		[1.]
15.57		[nan]
15.57142857		[1.]
15.6		[ 0.  1. nan]
15.61111111		[nan]
15.62222222		[1.]
15.625		[1.]
15.63375		[nan]
15.6375		[1.]
15.65714286		[1.]
15.6625		[1.]
15.66666667		[ 1. nan]
15.67571429		[1.]
15.68333333		[1.]
15.7		[1.]
15.7425		[nan]
15.74444444		[1.]
15.75		[ 1. nan]
15.77777778		[1.]
15.79857143		[1.]
15.8		[ 1. nan]
15.8125		[1.]
15.83333333		[1.]
15.85714286		[nan]
15.8625		[1.]
15.875		[1.]
15.88571429		[nan]
15.9		[1.]
15.91111111		[nan]
15.925		[1.]
15.94285714		[1.]
16.0		[ 0.  1. nan]
16.05		[nan]
16.075		[1.]
16.0875		[1.]
16.09375		[1.]
16.14285714		[nan]
16.16888889		[nan]
16.2		[ 1. nan]
16.28571429		[1.]
16.32727273		[1.]
16.33333333		[1.]
16.375		[1.]
16.37777778		[1.]
16.3875		[1.]
16.4		[ 1. nan]
16.42857143		[ 1. nan]
16.5		[ 1. nan]
16.575		[1.]
16.58222222		[1.]
16.6		[1.]
16.66666667		[1.]
16.8		[1.]
16.90909091		[1.]
17.0		[1.]
17.16666667		[1.]
17.42857143		[1.]
17.5875		[1.]
17.6		[1.]
17.69230769		[nan]
17.71428571		[1.]
18.57142857		[1.]
****************************************************************************************************
Curricular units 2nd sem (without evaluations)	Tuition fees up to date
0		[ 0.  1. nan]
1		[ 1.  0. nan]
2		[ 1.  0. nan]
3		[ 0.  1. nan]
4		[ 0.  1. nan]
5		[ 1.  0. nan]
6		[ 0.  1. nan]
7		[1. 0.]
8		[ 1.  0. nan]
12		[1. 0.]
****************************************************************************************************
Unemployment rate	Tuition fees up to date
7.6		[ 1.  0. nan]
8.9		[ 1.  0. nan]
9.4		[ 1.  0. nan]
10.8		[ 0.  1. nan]
11.1		[ 1.  0. nan]
12.4		[ 0.  1. nan]
12.7		[ 0.  1. nan]
13.9		[ 1.  0. nan]
15.5		[ 1.  0. nan]
16.2		[ 1.  0. nan]
****************************************************************************************************
Inflation rate	Tuition fees up to date
-0.8		[ 1.  0. nan]
-0.3		[ 1.  0. nan]
0.3		[ 1.  0. nan]
0.5		[ 0.  1. nan]
0.6		[ 1.  0. nan]
1.4		[ 0.  1. nan]
2.6		[ 1.  0. nan]
2.8		[ 1.  0. nan]
3.7		[ 0.  1. nan]
****************************************************************************************************
GDP	Tuition fees up to date
-4.06		[ 1.  0. nan]
-3.12		[ 1.  0. nan]
-1.7		[ 0.  1. nan]
-0.92		[ 1.  0. nan]
0.32		[ 1.  0. nan]
0.79		[ 1.  0. nan]
1.74		[ 0.  1. nan]
1.79		[ 0.  1. nan]
2.02		[ 1.  0. nan]
3.51		[ 1.  0. nan]
****************************************************************************************************
Target	Tuition fees up to date
Dropout		[ 0.  1. nan]
Enrolled		[ 1.  0. nan]
Graduate		[ 1.  0. nan]
****************************************************************************************************


df.head()


## Pie chart to look at Marital Status distribution
## Why Pie Chart and not some other chart? 
## -> Because we have already aware of number of classes in this column which are not many and pie chart is a good visualization


import plotly.graph_objects as go ## for Interactive Visualizations

df_pie_marital_status = df.groupby(by='Marital status')['ID'].count().reset_index()

fig = go.Figure(data=[
    go.Pie(labels = df_pie_marital_status['Marital status'],
           values = df_pie_marital_status['ID']
          )
])

fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=20,
                  marker=dict(line=dict(color='#000000', width=2)))
fig.update_layout(title = 'Marital Status Distribution amongst students')
fig.show()


fig = px.scatter(df, x = 'Age at enrollment', y='Marital status')
fig.update_layout(title = "Relationship b/w Age and Marital status",
                 xaxis_title = "Age",
                 yaxis_title = "Marital status")


## Bar chart to see the popularityy of courses taken
df_bar_course = df.groupby(by='Course')['ID'].count().reset_index()
px.bar(
    df_bar_course
    , x = 'Course'
    , y = 'ID'
)


## Handling the wrong datatype
df.loc[:, 'Course'] = df['Course'].astype('str')
## Bar plot
df_bar_course = df.groupby(by='Course')['ID'].count().reset_index().sort_values(by = 'ID', ascending = False)
df_bar_course.columns = ['Course', 'Count']
fig = px.bar(
    df_bar_course
    , x = 'Course'
    , y = 'Count'
)

fig.update_layout(title = "Course by Popularity",
                 xaxis_title = "Course",
                 yaxis_title = "Number of students who have taken this course")
fig.show()


## Pie chart to look at Attendence distribution
## Why Pie Chart and not some other chart? 
## -> Because we are already aware of number of classes in this column which are not many and pie chart is a good visualization

df_pie_attendence = df.groupby(by='Daytime/evening attendance')['ID'].count().reset_index()

fig = go.Figure(data=[
    go.Pie(labels = df_pie_attendence['Daytime/evening attendance'],
           values = df_pie_attendence['ID']
          )
])

fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=20,
                  marker=dict(line=dict(color='#000000', width=2)))
fig.update_layout(title = 'Daytime/evening attendance Distribution amongst students')
fig.show()


## Compare distribution of nationality with domestic/international students distribution

## Making Subplots
from plotly.subplots import make_subplots ## To Plot Subplots
fig = make_subplots(
    rows = 1
    , cols = 2
    , subplot_titles = ['Nationality Distribution', 'Student Distribution - (Domestic/International)']
    , specs=[[{"type": "bar"}, {"type": "pie"}]]
)

## Visualization for Nationality Distribution
df_bar_nationality = df.groupby(by='Nationality')['ID'].count().reset_index()
df_bar_nationality.columns = ['Nationality', 'Count']
## Adding visualization as a subplpot
fig.add_trace(
    go.Bar(name="Nationality Distribution"
           , x = df_bar_nationality['Nationality']
           , y = df_bar_nationality['Count'])
    , row = 1
    , col = 1
)


## Visualization for Domestic/International Distribution
df_pie_international = df.groupby(by='International')['ID'].count().reset_index()
df_pie_international.columns = ['International', 'Count']
## Adding visualization as a subplpot
fig.add_trace(
    go.Pie(
        labels = df_pie_international['International']
        , values = df_pie_international['Count']
        , name = 'Domestic/International Distribution'
          )
    , row = 1
    , col = 2
)


fig.update_layout(title = 'Distribution of nationality with domestic/international students distribution')

fig.update_xaxes(title_text="Nationality", row=1, col=1)
fig.update_yaxes(title_text="Count", row=1, col=1)

fig.show()


## Handing data inconsistancy for Nationality
df.loc[:, 'Nationality'] = df['Nationality'].astype('str')

## Making Subplots
fig = make_subplots(
    rows = 1
    , cols = 2
    , subplot_titles = ['Nationality Distribution', 'Student Distribution - (Domestic/International)']
    , specs=[[{"type": "bar"}, {"type": "pie"}]]
)

## Visualization for Nationality Distribution
df_bar_nationality = df.groupby(by='Nationality')['ID'].count().reset_index().sort_values(by = 'ID', ascending = False)
df_bar_nationality.columns = ['Nationality', 'Count']
## Adding visualization as a subplpot
fig.add_trace(
    go.Bar(name="Nationality Distribution"
           , x = df_bar_nationality['Nationality']
           , y = df_bar_nationality['Count'])
    , row = 1
    , col = 1
)


## Visualization for Domestic/International Distribution
df_pie_international = df.groupby(by='International')['ID'].count().reset_index()
df_pie_international.columns = ['International', 'Count']
## Adding visualization as a subplpot
fig.add_trace(
    go.Pie(
        labels = df_pie_international['International']
        , values = df_pie_international['Count']
        , name = 'Domestic/International Distribution'
        , hole = 0.5
        , marker=dict(
            colors=[ "#70C1B3", "#79b159"]
            , line=dict(color='#000000', width=2)
        )
    )
    , row = 1
    , col = 2
)


fig.update_layout(title = 'Distribution of nationality with domestic/international students distribution')

fig.update_xaxes(title_text="Nationality", row=1, col=1)
fig.update_yaxes(title_text="Count", row=1, col=1)

fig.show()


## Visualize data distribution for the following attributes: Displaced, Educational special needs, Debtor, Scholarship holder

## Visualization Library built on top of matplotlib
import seaborn as sns

## defining Seaborn color palette to use
colors = sns.color_palette('pastel')

## Making subplots
fig = plt.figure(figsize=(18,10), dpi=1600)


## Distribution for 'Displaced' Attribute
df_pie_Displaced = df.groupby(by='Displaced')['ID'].count().reset_index().sort_values(by = 'ID', ascending = False)
df_pie_Displaced.columns = ['Displaced', 'Count']
## Adding visualization as a subplpot
ax1 = plt.subplot2grid((2,2),(0,0)) ## Row1 Col1
plt.pie(x = df_pie_Displaced['Count'], labels = df_pie_Displaced['Displaced'], colors = colors[:2], autopct='%.0f%%')
plt.title('Displaced Distribution')


## Distribution for 'Educational special needs' Attribute and Adding visualization as a subplpot
df_pie_Special_Needs = df.groupby(by='Educational special needs')['ID'].count().reset_index().sort_values(by = 'ID', ascending = False)
df_pie_Special_Needs.columns = ['Educational special needs', 'Count']
ax1 = plt.subplot2grid((2,2),(0,1)) ## Row1 Col2
plt.pie(x = df_pie_Special_Needs['Count'], labels = df_pie_Special_Needs['Educational special needs'], colors = colors[2:4], autopct='%.0f%%')
plt.title('Educational special needs Distribution')

## Distribution for 'Debtor' Attribute and Adding visualization as a subplpot
df_pie_Debtor = df.groupby(by='Debtor')['ID'].count().reset_index().sort_values(by = 'ID', ascending = False)
df_pie_Debtor.columns = ['Debtor', 'Count']
ax1 = plt.subplot2grid((2,2),(1,0)) ## Row2 Col1
plt.pie(x = df_pie_Debtor['Count'], labels = df_pie_Debtor['Debtor'], colors = colors[4:6], autopct='%.0f%%')
plt.title('Debtor Distribution')

## Distribution for 'Scholarship holder' Attribute and Adding visualization as a subplpot
df_pie_Scholarship = df.groupby(by='Scholarship holder')['ID'].count().reset_index().sort_values(by = 'ID', ascending = False)
df_pie_Scholarship.columns = ['Scholarship holder', 'Count']
ax1 = plt.subplot2grid((2,2),(1,1)) ## Row2 Col2
plt.pie(x = df_pie_Scholarship['Count'], labels = df_pie_Scholarship['Scholarship holder'], colors = colors[6:8], autopct='%.0f%%')
plt.title('Scholarship holder Distribution')


## Updting figure title
fig.suptitle('Data Distribution in different attributes')
plt.show()


## Visualize data distribution for Gender and Target Attributes

## Making subplots
fig = plt.figure(figsize=(18,6), dpi=1600)

## Distribution for 'Gender' Attribute and Adding visualization as a subplpot
df_pie_Gender = df.groupby(by='Gender')['ID'].count().reset_index().sort_values(by = 'ID', ascending = False)
df_pie_Gender.columns = ['Gender', 'Count']
ax1 = plt.subplot2grid((1,2),(0,0)) ## Row1 Col1
plt.pie(x = df_pie_Gender['Count'], labels = df_pie_Gender['Gender'], colors = colors[:2], autopct='%.0f%%')
plt.title('Gender Distribution')

## Distribution for 'Target' Attribute and Adding visualization as a subplpot
df_pie_Target = df.groupby(by='Target')['ID'].count().reset_index().sort_values(by = 'ID', ascending = False)
df_pie_Target.columns = ['Target', 'Count']
ax1 = plt.subplot2grid((1,2),(0,1)) ## Row1 Col2
plt.pie(x = df_pie_Target['Count'], labels = df_pie_Target['Target'], colors = colors[2:], autopct='%.0f%%')
plt.title('Target Distribution')

## Updting figure title
fig.suptitle('Data Distribution in Gender and Target attributes')
plt.show()


df_Nationality_metrics = df.groupby('Nationality')[['Unemployment rate', 'Inflation rate', 'GDP']].mean().reset_index().sort_values(by='GDP', ascending = False)
fig = go.Figure()

## GDP Visualization
fig.add_trace(
    go.Bar(
        x=df_Nationality_metrics['Nationality']
        , y=df_Nationality_metrics['GDP']
        , name='GDP'
    )
)

## Inflation rate Visualization
fig.add_trace(
    go.Bar(
        x=df_Nationality_metrics['Nationality']
        , y=df_Nationality_metrics['Inflation rate']
        , name='Inflation rate'
    )
)

## Unemployment rate Visualization
fig.add_trace(
    go.Bar(
        x=df_Nationality_metrics['Nationality']
        , y=df_Nationality_metrics['Unemployment rate']
        , name='Unemployment rate'
    )
)

# Here we modify the tickangle of the xaxis, resulting in rotated labels.
fig.update_layout(barmode='group', xaxis_tickangle=-45, title = "GDP, Inflation rate and Unemployment by country")
fig.show()


## Relationship between (GDP - Unemployment Rate) and (GDP - Inflation Rate) and (Inflation Rate - Unemployment Rate)
fig = px.scatter_matrix(df_Nationality_metrics.drop('Nationality', axis = 1))
fig.update_traces(
    diagonal_visible=False
    , marker=dict(size=1, line=dict(width=1, color="DarkSlateGrey"))
)
fig.update_layout(
    title = 'Relationship between GDP, Unemployment Rate and Inflation Rate'
)
## Descriptive Statistic : Correlation Metrix
bold = '\033[1m'
end = '\033[0m'
print(bold, df_Nationality_metrics.corr(), end)
fig.show()

                    Unemployment rate  Inflation rate       GDP
Unemployment rate           1.000000        0.051397 -0.702510
Inflation rate              0.051397        1.000000 -0.437959
GDP                        -0.702510       -0.437959  1.000000


## Data Distribution of Age atttribute by Genders
fig = px.histogram(df, x='Age at enrollment', color='Gender',
            barmode = 'overlay', nbins=50, marginal = 'box', color_discrete_sequence = ["#79B159", "#FFB482"])
fig.update_layout(title = "Age distribution by Gender.",
                 xaxis_title = "Age",
                 yaxis_title = "No of students")

fig.show()


## Scatterplot is a good visualization to see the trend and relationship between grades of two semester
fig = px.scatter(
    x = df['Curricular units 1st sem (grade)']
    , y = df['Curricular units 2nd sem (grade)']
    , color_discrete_sequence = ["#79b159"]
    , hover_name = df['ID']
)
fig.update_layout(title = "Relationship between 1st sem grades and 2nd sem grades",
                 xaxis_title = "First Sem Grades",
                 yaxis_title = "Second Sem Grades")
print(
    "Descriptive Statistics: Correlation between 1st sem grades and 2nd sem grades: "
    , bold
    , df.corr().loc['Curricular units 1st sem (grade)', 'Curricular units 2nd sem (grade)']
    , end
)
fig.show()

Descriptive Statistics: Correlation between 1st sem grades and 2nd sem grades:   0.8381597266089426


fig = px.scatter_matrix(df.loc[:, ['Previous qualification (grade)', 'Curricular units 1st sem (grade)', 'Curricular units 2nd sem (grade)']])
fig.update_traces(
    diagonal_visible=False
    , marker=dict(size=1, line=dict(width=1, color="DarkSlateGrey"))
)
fig.update_layout(
    title = 'Relationship between grades'
    , font_size = 7
)
print(
    bold
    , "Descriptive Statistics Correlation among grades:"
    , end
    , '\n'
    , df.loc[:, ['Previous qualification (grade)', 'Curricular units 1st sem (grade)', 'Curricular units 2nd sem (grade)']].corr()[['Previous qualification (grade)']]
)
fig.show()

 Descriptive Statistics Correlation among grades:  
                                   Previous qualification (grade)
Previous qualification (grade)                          1.000000
Curricular units 1st sem (grade)                        0.061213
Curricular units 2nd sem (grade)                        0.055099


six_grid_cols = ['Unemployment rate', 'Inflation rate', 'GDP', 'Previous qualification (grade)', 'Curricular units 1st sem (grade)', 'Curricular units 2nd sem (grade)']
fig = px.scatter_matrix(df.loc[:, six_grid_cols])
fig.update_traces(
    diagonal_visible=False
    , marker=dict(size=1, line=dict(width=1, color="DarkSlateGrey"))
)
fig.update_layout(
    title = 'Relationship between numerical attributes.'
    , font_size = 7
) ## should read country attributes seperately and student performance attributes seperately
fig.show()


corr_six_grid = df.loc[:, six_grid_cols].corr()
px.imshow(corr_six_grid, color_continuous_scale = 'twilight')

	ID;Marital status;Application mode;Application order;Course;Daytime/evening attendance;Previous qualification;Previous qualification (grade);Nationality;Mother's qualification;Father's qualification;Mother's occupation;Father's occupation;Admission grade;Displaced;Educational special needs;Debtor;Tuition fees up to date;Gender;Scholarship holder;Age at enrollment;International
0	2541;1;7;1;9147;1;3;120;1;1;39;4;7;120;0;0;0;1...
1	2554;1;17;1;9991;0;1;160;1;34;34;0;0;161.8;1;0...
2	2790;1;39;1;171;1;1;133.1;1;37;37;9;7;100;0;0;...
3	2799;1;42;1;9500;1;1;150;1;19;1;9;3;110;1;0;0;...
4	2883;2;39;1;9991;0;1;133.1;1;37;37;9;9;113.4;0...

COS60008 Introduction to Data Science¶

Assignment 1, 2023, Semester 1¶

Student Details:¶

Table Of Contents¶

Task Description ¶

Task 1 - Data Acquisition & Preperation ¶

Task 2 - Data Exploration ¶

References ¶

¶

Task Description¶

Task 1 - Data Acquisition & Preperation¶

Approach:¶

Task 1.1 - Load¶

Task 1.2 - Merge¶

Task 1.3 - Clean¶

Approach for numerical attributes¶

Task 2 - Data Exploration¶

Approach:¶

Task 2.1 - Data Distributions¶

Task 2.2 - Relationships among attributes¶

Task 2.3 - Scatter Matrix¶

References¶

	ID	Marital status	Application mode	Application order	Course	Daytime/evening attendance	Previous qualification	Previous qualification (grade)	Nationality	Mother's qualification	...	Father's occupation	Admission grade	Displaced	Debtor	Tuition fees up to date	Gender	Scholarship holder	Age at enrollment
0	2541	1	7	1	9147	1	3	120.0	1	1	...	7	120.0	0	0	1	0	0	333
1	2554	1	17	1	9991	0	1	160.0	1	34	...	0	161.8	1	1	1	1	0	333
2	2790	1	39	1	171	1	1	133.1	1	37	...	7	100.0	0	1	1	1	1	333
3	2799	1	42	1	9500	1	1	150.0	1	19	...	3	110.0	1	0	1	1	1	333
4	2883	2	39	1	9991	0	1	133.1	1	37	...	9	113.4	0	0	1	0	0	333

	ID	Curricular units 1st sem (enrolled)	Curricular units 1st sem (evaluations)	Curricular units 1st sem (approved)	Curricular units 1st sem (grade)	Curricular units 2nd sem (enrolled)	Curricular units 2nd sem (evaluations)	Curricular units 2nd sem (approved)	Curricular units 2nd sem (grade)	Unemployment rate	Inflation rate	GDP	Target
0	1	0	0	0	0.0	0	0	0	0.0	10.8	1.4	1.74	Dropout
1	3	6	0	0	0.0	6	0	0	0.0	10.8	1.4	1.74	Dropout
2	8	5	5	0	0.0	5	5	0	0.0	15.5	2.8	-4.06	Dropout
3	10	6	9	5	11.4	6	14	2	13.5	8.9	1.4	3.51	Dropout
4	13	6	6	0	0.0	6	0	0	0.0	12.7	3.7	-1.70	Dropout

	ID	Marital status	Application mode	Application order	Course	Daytime/evening attendance	Previous qualification	Previous qualification (grade)	Nationality	Mother's qualification	Father's qualification	Mother's occupation	Father's occupation	Admission grade	Displaced	Debtor	Scholarship holder	Age at enrollment	International	Curricular units 1st sem (credited)	Curricular units 1st sem (enrolled)	Curricular units 1st sem (evaluations)	Curricular units 1st sem (approved)	Curricular units 1st sem (grade)	Curricular units 1st sem (without evaluations)	Curricular units 2nd sem (credited)	Curricular units 2nd sem (enrolled)	Curricular units 2nd sem (evaluations)	Curricular units 2nd sem (approved)	Curricular units 2nd sem (grade)	Unemployment rate	Inflation rate	GDP	Target
0	239	1	1	1	9130	1	1	126.0	1	19	1	9	9	122.5	1	0	0	25	NaN	0	5	14	4	13.50	2	0	5	10	4	14.250000	9.4	-0.8	-3.12	Dropout
1	240	1	39	1	9085	1	19	133.1	1	19	19	9	7	140.0	0	1	0	24	NaN	2	6	11	3	11.75	0	1	6	14	3	10.333333	16.2	0.3	-0.92	Dropout
2	244	1	39	1	9147	1	1	120.0	1	37	1	9	1	122.7	0	0	0	25	NaN	0	5	5	0	0.00	0	0	5	9	0	0.000000	10.8	1.4	1.74	Dropout
3	248	1	44	1	9003	1	39	130.0	1	37	37	9	10	130.0	0	1	0	29	NaN	0	6	13	3	12.75	0	0	6	8	3	11.333333	13.9	-0.3	0.79	Dropout
4	256	2	39	1	9556	1	19	133.1	1	38	37	7	7	99.5	1	1	1	37	NaN	0	7	13	1	16.00	0	0	7	12	0	0.000000	8.9	1.4	3.51	Dropout

	ID	Marital status	Application mode	Application order	Course	Daytime/evening attendance	Previous qualification	Previous qualification (grade)	Nationality	Mother's qualification	Father's qualification	Mother's occupation	Father's occupation	Admission grade	Debtor	Tuition fees up to date	Gender	Age at enrollment	Curricular units 1st sem (credited)	Curricular units 1st sem (enrolled)	Curricular units 1st sem (evaluations)	Curricular units 1st sem (approved)	Curricular units 1st sem (grade)	Curricular units 2nd sem (credited)	Curricular units 2nd sem (enrolled)	Curricular units 2nd sem (evaluations)	Curricular units 2nd sem (approved)	Curricular units 2nd sem (grade)	Unemployment rate	Inflation rate	GDP	Target
17	690	4	39	2	9147	1	19	133.1	1	37	37.0	9	7	116.4	1	0.0	1	70	0	5	5	0	0.000000	0	5	9	0	0.000000	10.8	1.4	1.74	Dropout
18	2960	5	39	1	9254	1	1	133.1	1	2	2.0	2	2	103.0	0	1.0	0	62	0	6	7	6	15.000000	0	6	10	6	12.833333	10.8	1.4	1.74	Graduate
19	950	1	39	1	9147	1	1	140.0	1	37	3.0	9	2	135.1	0	1.0	1	61	0	5	0	0	0.000000	0	5	0	0	0.000000	15.5	2.8	-4.06	Dropout
20	708	2	39	1	9003	1	1	150.0	1	37	37.0	9	6	140.0	0	1.0	1	60	17	18	18	18	14.111111	11	13	13	13	14.230769	15.5	2.8	-4.06	Graduate
21	2868	1	39	1	9003	1	19	133.1	1	37	37.0	9	9	100.0	0	1.0	0	60	0	6	10	6	12.750000	0	6	6	4	14.750000	13.9	-0.3	0.79	Graduate